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AI Agent Claude Gemini GPT MCP for MVP.
AI Agent Claude Gemini GPT MCP for MVP

Hello!

I'd like to invite you to take a look at the job I've posted. Please submit a proposal if you're available and interested. My character has a lot of tattoos and i am having trouble making her tattoos stick in cloned videos, nsfw content, etc. Let me know if you are able to train her for me. I have pictures of multiple angles of my model

Iva N.

must be able to teach these topics, please read the paper

We are a founder-led startup developing a private enterprise AI platform. We are looking for a technically strong, hands-on engineer who can improve an existing system, lead a small engineering team and help us take the product substantially further.

This role combines production engineering, applied AI innovation and technical leadership. You must be comfortable writing code, reviewing others’ work and taking responsibility for delivery.

Product functionality, target market and detailed architecture will be shared with shortlisted candidates under confidentiality arrangements.

The opportunity

Our platform is under active development. We need someone who can understand the existing codebase, identify what should be retained or improved, and turn ambitious product objectives into reliable software.

Beyond resolving immediate engineering challenges, we want someone who can recognise emerging AI capabilities and propose meaningful advances in what the platform can achieve. You should bring original thinking, technical depth and the ability to demonstrate ideas through working implementations.

What you will do

Assess the existing architecture, distinguish structural weaknesses from isolated defects and recommend prioritised improvements.

Design and implement reliable AI orchestration, tool execution and multi-step workflows.

Establish appropriate state management, validation, retries, recovery and execution boundaries.

Decide where LLM reasoning adds value and where deterministic code is required.

Build repeatable evaluations and trace failures across models, prompts, application code and integrations.

Translate business objectives into technical plans, clear acceptance criteria and achievable delivery commitments.

Write production code, review pull requests and help colleagues resolve difficult engineering problems.

Take responsibility for release quality, observability, deployment practices and operational documentation.

Explain technical options, risks and trade-offs clearly to the founder.

Innovation and advanced AI capability

We want someone who can help move the platform beyond conventional chat interfaces and basic agent demonstrations.

You should be able to evaluate and apply advances in areas such as:

Agents that plan, execute, verify results and recover from failure across longer tasks.

Persistent context, memory and retrieval that improve relevance while preserving information boundaries.

Coordinated agents and tools, with clear responsibilities and controlled execution.

Proactive assistance and background workflows, with appropriate permissions and human oversight.

Model selection and routing to improve quality, latency and cost.

Interfaces that allow users to understand, direct and intervene in AI-driven work.

You will be expected to propose improvements tied to user needs, build focused experiments, measure their results and recommend what should enter production. We value the judgement to distinguish useful advances from impressive demonstrations that do not hold up in practice.

Required qualifications and experience

A recognised university degree in Artificial Intelligence, Machine Learning, Computer Science or a closely related technical discipline.

If your degree is in a broader discipline, substantive formal study or postgraduate qualifications in AI or machine learning.

At least five years of professional software engineering experience, including responsibility for production systems.

Demonstrable experience building and maintaining LLM-based applications used by real users.

Strong backend engineering skills and practical knowledge of APIs, databases, cloud infrastructure and software security.

Experience with tool calling, structured outputs, retrieval and agent orchestration.

Experience leading or mentoring engineers while remaining a substantial individual contributor.

Ability to read technical papers or documentation critically and translate relevant ideas into practical implementations.

Short courses, prompt-engineering certificates or experience using coding assistants alone do not meet these requirements.

How we expect you to lead

For a small team, technical leadership means contributing directly while taking responsibility for the wider engineering outcome.

You should review implementation details, challenge weak assumptions, identify problems early and help colleagues succeed. When requirements are unclear, we expect you to ask specific questions and resolve ambiguity. When an approach is failing, we expect you to propose and implement a better route.

We value clear communication, constructive disagreement and evidence-based decisions.

Working arrangement

Long-term freelance engagement requiring full-time availability.

Meaningful working-hours overlap with the UK.

Please disclose other ongoing professional commitments.

An initial paid, scoped technical assessment and practical contribution, followed by a longer engagement if there is a strong fit.

Confidentiality arrangements before access to sensitive product information or code.

Please include the following in your application

Your relevant degree and AI qualifications, including institutions and subjects studied.

Two production AI systems you helped build: your personal contribution, how they were used and what you maintained after launch.

One example of diagnosing and fixing an unreliable AI workflow. Explain how you identified the cause and verified the improvement.

One example of an innovative AI capability you proposed and implemented. What measurable benefit did it create?

How you would approach your first two weeks joining an existing platform and engineering team.

Relevant code samples, technical writing or work you can demonstrate confidentially.

Your earliest start date, weekly availability, UK-hours overlap, other commitments and expected rate.

Please provide specific examples and distinguish your own work from the wider team’s contribution. Applications without the required qualifications or relevant production experience will not be shortlisted.

I’m building software for established coaches and consultants that writes their messaging and runs their Meta ads. One piece is a research engine that finds how a coach’s buyers actually talk about their problem online, and I’m building it myself with Claude Code. I don’t need you to build it. I need someone who has done this before to show me how to create this research engine so it provides useful results. It's not just a prompt in Claude, it has to go out and scrape Reddit, YouTube, Social Media, Ads in Meta Ads library etc to find real comments and statements.

What it has to produce: a short brief on a buyer market that shows what they’re frustrated with, what they’ve tried, what happened right before they went looking for help, their objections, and their exact phrases, with counts and every quote linked to its source.

What I’d want from you:
Show me how to set up the scrapers or other tools to do to get the desired output.
Show me how you’d sort real buyers from noise, and how to make sure no made-up quote gets through
Help me judge whether a test brief is good enough, and how much data is enough

To make sure you understand this job and the details in the first line of your proposal put the words Elvis Presley.

Project Domain / Category
Artificial Intelligence / Computer Vision / Medical Image Processing / Deep Learning
Abstract / Introduction
Brain tumor detection from Magnetic Resonance Imaging (MRI) is an important medical image
analysis task. Deep learning techniques can assist in identifying abnormal patterns in brain MRI
images. Capsule Networks (CapsNet) offer an alternative to conventional Convolutional Neural
Networks (CNNs) by learning spatial relationships among image features. Integrating a trained
deep learning model into a web-based application can make MRI image analysis more
accessible and convenient for demonstration and educational purposes.
This project aims to develop a web-based brain tumor detection system using a Capsule
Network (CapsNet). The publicly available Br35H Brain MRI dataset, containing Tumor and NoTumor images, will be used for model training and testing. The developed web application will
allow users to upload MRI images, perform basic image-quality assessment, and obtain Tumor
or No Tumor predictions using the trained CapsNet model.
Functional Requirements: In this project, we will leverage image processing and deep learning
techniques to address the following objectives:
1. Data Collection and Preprocessing: Gather a diverse dataset of brain MRI scans,
ensuring data quality and integrity. Preprocess the images to enhance their suitability
for further analysis. Apply image normalization, resizing, and suitable data
augmentation.
2. Dataset Splitting: To facilitate model training and evaluation, we will split the dataset
into distinct sets for training, validation, and testing. This step ensures that the deep
learning model's performance is rigorously assessed and prevents overfitting.
3. Deep Learning Model Development: Implement and train a conventional Convolutional
Neural Network (CNN) as a baseline model and a Capsule Network (CapsNet) as the
primary model for classifying brain MRI images into Tumor and No Tumor categories.
Conduct a comprehensive comparative performance analysis of both models using
Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Confusion Matrix. The comparison
shall evaluate their classification effectiveness, ability to correctly identify tumor cases,
and computational efficiency to determine the relative strengths and limitations of
CapsNet compared with the conventional CNN.
4. Model Evaluation: We will establish robust evaluation metrics to assess the
performance of the deep learning models, ensuring that the segmentation results are
precise and reliable.
Evaluate the model using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and
Confusion Matrix.
Compare CapsNet with a basic CNN model to determine whether Capsule Networks
provide any performance advantage. Analyze the performance drop of each model
under different image-quality degradations.
5. DataSet: For this project you need to use BR35H Brain MRI dataset.
6. Model Deployment: Students shall develop a user-friendly web interface that allows
users to upload brain MRI images. The uploaded MRI image shall be processed using the trained CapsNet model. The system shall classify the image as Tumor or No Tumor and
display the predicted class and model score

Skills: Python, Image Processing, OpenCV, Flask, Tensorflow, Keras, Pytorch, Computer Vision, Deep Learning, Web Application

need to deploy ai agent solution should have option to add many agents in future. what will be your charge

Hello, we have developed a pipeline to create scaled human 3D models from an iOS device. We have our own 3DGS, meshing and ARKit scaling solution, however we need help refining the process to match 3rd party system performance. Are you the right person to help? We would love to chat and explore the project. Regards, James

I’m developing an AI-powered loss-prevention system for delis and convenience stores.

I own a deli in Brooklyn, NY, and want to build an MVP using our existing security cameras. The goal is to use computer vision to identify potentially suspicious activities involving cashiers and customers, such as unusual register activity, potential employee theft, and shoplifting.

The system should ideally:

* Connect to existing IP cameras and DVR/NVR systems using RTSP or ONVIF where supported.
* Analyze video footage using AI.
* Detect and flag specific suspicious events.
* Save short video clips with timestamps for review.
* Provide a simple dashboard and alerts for business owners.
* Eventually support multiple cameras and multiple business locations.

I’m looking for an experienced computer vision engineer with a background in CCTV, real-time video analytics, and AI surveillance systems.

I want to start with a small technical feasibility project using my own store’s cameras before investing in a full commercial platform.

I’m interested in your recommended technical approach, estimated timeline, and budget for an initial MVP.

The goal is to build a reliable, scalable product that can eventually be offered to other small businesses through a monthly subscription.

I’m looking for an experienced AI computer vision developer to help build an MVP for an AI-powered loss-prevention service for independent grocery stores and super markets.

The concept:

* Analyze footage from existing CCTV cameras and DVR/NVR systems.
* Detect potentially suspicious cashier behavior, such as concealing cash, suspicious handoffs, or unusual product handling.
* Send timestamped alerts and video clips to the store owner.
* Work without requiring a POS system.
* Avoid replacing existing cameras wherever technically possible.

Required experience:

* Computer vision and video analytics.
* Python, OpenCV, and relevant AI video models.
* RTSP, IP cameras, and DVR/NVR integrations.
* Real-time video processing and alert systems.
* Deploying AI locally on an edge device or in the cloud.

First milestone:
I want a technical feasibility assessment and a small proof of concept using sample deli footage before committing to a full product.

Please share examples of similar systems you’ve built, your proposed technical approach, estimated cost, and timeline.

I need a full, end-to-end study that benchmarks both classical machine-learning techniques and modern deep-learning architectures on the FER2013 dataset. The work starts with thoughtful preprocessing, continues through careful hyper-parameter tuning, and finishes with a thorough academic-style analysis.

Scope of Work
• Run an ablation study so we understand the impact of each preprocessing step and model component.
• Train and validate multiple models, then evaluate them with Accuracy, F1-score, Recall, and detailed confusion matrices.
• Carry out a data-efficiency test (using just 10 % of the training set versus the full 100 %).
• Test robustness by adding Gaussian noise, blur, and gamma shifts at three severity levels.
• Compare models on performance metrics, training time, and inference speed, wrapping everything in clear visualisations and commentary.

Deliverables
1. Well-commented Python code in a Jupyter Notebook.
2. Trained weight files for every model.
3. All raw results, plots, and figures.
4. A comprehensive academic report in English explaining methodology, findings, and future work.
5. A concise PowerPoint deck that highlights key insights.

I am looking for reproducible work, clean structure, and insightful discussion that helps researchers and practitioners alike understand how classical and deep methods stack up on facial-expression recognition.

Skills: Python, Statistics, Machine Learning (ML), Statistical Analysis, Data Visualization, Data Analysis, Deep Learning, Facial Recognition

We are looking for an experienced Computer Vision & AI Software Engineer to develop, fine-tune, and deploy a custom real-time object detection system using YOLOv8.
The goal of this project is to build an end-to-end solution—from fine-tuning a YOLOv8 model on custom datasets to deploying a lightweight backend API and Web interface for live inference (image/video/webcam streams).
Key Responsibilities
• Data Preparation & Fine-Tuning: Preprocess, augment, and split custom image/video datasets to fine-tune a YOLOv8 model (e.g., target classes such as PPE compliance: Hardhat, No-Hardhat, Safety Vest, No-Safety Vest, Mask, Person, etc.).
• Model Training & Evaluation: Train and evaluate the model using PyTorch and Ultralytics YOLOv8. Monitor loss curves and report metrics including [email protected], [email protected]:0.95, Precision, and Recall.
• Backend API Development: Build a clean FastAPI backend endpoint (/predict) to handle image upload / video frame stream requests and return inference results with bounding boxes (e.g., encoded as Base64 or JSON bounding box formats).
• Frontend Web & Live Stream Integration: Create a simple HTML/JavaScript frontend supporting image uploads, local video file processing, and live webcam stream detection.
• Model Optimization & Export: Export the trained model to production-ready formats (e.g., ONNX, TensorRT, or .pt) and document deployment steps.
Required Skills & Qualifications
• Machine Learning / CV: 2+ years experience in Object Detection, Deep Learning, and Computer Vision (OpenCV, PyTorch, Ultralytics YOLOv8).
• Backend Development: Strong proficiency in Python and FastAPI.
• Frontend Basics: Basic understanding of JavaScript/HTML for handling web assets and canvas bounding box rendering.
• Model Evaluation: Ability to deliver detailed evaluation reports (Confusion Matrix, PR curves, F1 curves).
Deliverables
1. Trained Weights & Model Checkpoints: Optimized .pt and ONNX weights hosted or uploaded to Hugging Face / GitHub releases.
2. Training Artifacts & Notebook: Annotated Jupyter notebook (YOLO_Training.ipynb) showing dataset prep, loss curves, confusion matrix, and evaluation metrics.
3. End-to-End Application Codebase:
o Backend (backend/app.py): FastAPI inference server.
o Frontend (index.html, webcam.html): Web interface for upload and real-time webcam monitoring.
o Local Script (local/webcam_detection.py): Python script for local live webcam stream testing.

NO PLACEHOLDER BIDS, YOU WILL BE BLOCKED.

Hiring an editor to swap faces for a realistic deepfake video.

https://youtu.be/l5m4bPQLPZ8?si=br2GdMtsw5NavpnF

- With the link above, you will swap faces/bodies with selective characters.

Here is an example of the realism.

Original version:
https://youtube.com/shorts/bS0TMK4OIJI?si=6lwT5AFYx14aaSf9

Deepfake version:
https://www.instagram.com/reel/DeM2Q_PgbJn/?exln=MWt1Nzd1NnA4NmFzeQ==

Do not apply if your turnaround is long than 5 days.
Do not apply if your fee is in the 100’s.

Please provide samples of your work. Your work must depict realism.

Skills: Animation, After Effects, 3D Animation, Video Editing, Deep Learning, AI Video, AI Image Generation, AI Video Editing

Development of a computer-vision AI model for football match video analysis, focused on tracking a single selected player and extracting performance metrics.

Scope of work:
- Player detection model (YOLO) trained/fine-tuned on football broadcast footage
- Multi-object tracking (ByteTrack) with single-player lock-on and ID-switch handling
- Two-stage processing: fast preview for player selection, then full GPU analysis
- Pitch calibration to convert video coordinates into real-world positions
- Metrics extraction: distance covered, speed, sprints, activity levels (standing to sprinting), positional heatmap and zone occupancy
- Structured JSON output ready to feed reporting and AI-generated analysis
- GPU inference pipeline (serverless) with testing and accuracy tuning

This contract covers the AI model and analysis pipeline only. The web platform, user interface and report design are not included.

We need a freelancer to train, evaluate, and fine-tune AI models and chatbots for our projects. The work includes improving model performance, testing responses, and refining outputs to meet project goals. You should be comfortable working with AI systems, analyzing results, and making adjustments to improve accuracy and effectiveness. This is a part-time role for someone who can support ongoing AI development and help ensure models deliver reliable, high-quality results.

We are seeking an expert to develop a full-stack solution for a small language model (SLM) application. The role includes building the backend, frontend, and API architecture, while also implementing advanced machine learning and deep learning techniques to improve model performance. You will work on optimization strategies, data processing pipelines, and scalable deployment solutions. The ideal freelancer should be able to deliver a complete, production-ready system with strong performance, reliability, and maintainability.

O que contratar primeiro

**Especialista em visão computacional para futebol, rastreamento de múltiplos jogadores e reconhecimento temporal de ações.** Procurar experiência demonstrável em Python, PyTorch, OpenCV, detecção de objetos, associação de trajetórias e avaliação de vídeo. Conhecimento de YOLO/BoT-SORT e reidentificação por aparência pode facilitar o aproveitamento da base; trocar componentes exige comparação e justificativa.

Não exigir uma LLM própria ou um modelo generativo como solução inicial. O problema atual envolve detecção, associação temporal, identidade, bola e classificação. A arquitetura pode combinar modelos especializados. O produto deve executar a análise sem chamadas ao Gemini/OpenAI ou a outro serviço de compreensão visual; hospedagem em GPU é infraestrutura substituível.

## Base que deve ser reaproveitada

| Ativo existente | O que oferece | O que ainda falta |
|---|---|---|
| Detector de pessoas, rastreador e pose | Observações de vários jogadores, caixas e IDs locais | Continuidade confiável em sobreposição, saída/retorno e cortes |
| Detector próprio de bola refinado por 42 épocas | Caixas e observações da bola em vídeos de celular | Cobertura em bola pequena, borramento, oclusão e bola aérea; bola detectada não comprova posse |
| Classificador de ações e refinamentos anteriores | Propostas de passe, condução, finalização e outras classes | Reduzir falsos positivos e ações perdidas; ligar ação ao jogador correto |
| JSONs auxiliares e revisões humanas | Supervisão já aproveitada em treinos próprios | Não são trajetórias densas revisadas de todos os jogadores nem de remetente/receptor |
| Corpus histórico de ações | Dez fontes, 49 positivos e 15 negativos específicos; versão agrupada com 70 grupos | Poucos exemplos e fontes por classe; 70 grupos não são 70 vídeos independentes |
| Contexto e índice de participantes | Evidências por frame e propostas ligadas a atores possíveis | Tracklet não equivale a pessoa global ou perfil; autoria e recepção ainda ambíguas |
| Worker Python e contrato TypeScript | Processamento de pesquisa e JSON estruturado | Integração durável ao upload e aceite de produto são entregas posteriores |
| Backup local | Pesos, corpus, revisões, relatórios e configurações preservados | Fornecer pacote mínimo selecionado; não repassar todo o backup |

Os 11.327 rótulos de bola registrados no intake pertencem à tarefa de bola, não são 11.327 ações. As revisões de identidade disponíveis são esparsas e não substituem anotação de trajetórias. Quantidades finais elegíveis e linhagem devem ser conferidas no inventário do primeiro marco.

## O que precisa de treino e o que precisa de engenharia

| Frente | Trabalho necessário | Prioridade |
|---|---|---|
| Identidade e rastreamento | Diagnosticar associação, estado durante oclusão e recuperação; avaliar se embeddings de aparência precisam de adaptação com pares/identidades anotados | Primeira contratação |
| Bola | Avaliar o checkpoint existente por tipo de erro; se necessário, refinar com caixas revisadas de bola pequena/aérea/oculta e negativos explicitamente anotados | Condicionada ao erro medido |
| Contato e transição entre jogadores | Anotar/evaluar jogador que toca, instante, bola livre, receptor e incerteza; produzir candidatos sem depender apenas do atleta inicial | Segunda etapa |
| Ações temporais | Refinar passe, condução e finalização com clipes/intervalos revisados, máscaras de desconhecido e negativos por classe; avaliar gol separadamente | Segunda etapa |
| Autoria e contagens | Associar evento, autor e receptor a IDs reconciliados; separar tentativa, conclusão e resultado desconhecido | Após evidência de identidade e ação |
| Backend e melhoria contínua | Versionar dataset/modelo, registrar inferência, preservar revisões e avaliar candidatas antes de promoção | Entrega de reprodução; integração do app orçada à parte |

Falta, desarme, drible contra adversário e gol não devem herdar automaticamente o resultado de uma classe parecida. Condução não prova drible bem-sucedido. Bola perto do jogador não prova domínio. Finalização não prova gol. Um gol fora de quadro pode ficar desconhecido.

Skills: Python, Data Processing, OpenCV, Pytorch, Computer Vision, Deep Learning, Object Detection, YOLO, Data Annotation, Model Evaluation

We’re looking for an experienced AI Engineer to analyze and optimize our core agentic pipeline for a medical SaaS platform. Our system assists healthcare providers with medical diagnosis decisions, structures clinical notes, and streamlines patient management workflows.
Because this is a clinical decision-support tool, the main goal is to drastically reduce response time (latency) and operating costs while strictly maintaining or improving the precision, safety, and quality of the AI's outputs.
The system is a multi-stage agentic pipeline that processes complex patient data, electronic health records (EHR), and queries external medical knowledge bases (PubMed, clinical guidelines). It is used daily by doctors and medical staff to find relevant research, summarize patient histories, and cross-reference diagnostic criteria.

What we’ve already done:

The pipeline is in production, and we’ve already implemented several baseline optimizations:
• Medical Guardrails: Implemented strict clinical guardrails for input/output validation to eliminate hallucinations and flag unsafe outputs.
• Rigorous Evals: Set up structured clinical evaluation filters to verify medical accuracy.
• Model Routing: Introduced dynamic routing (directing simpler synthesis tasks to smaller models, and complex diagnostic reasoning to frontier models).

Now we need a specialist to take it to the next level with a systematic, data-driven optimization focused on speed and cost reduction without sacrificing clinical safety.

What you’ll do

• Audit the current pipeline architecture, agent orchestration, and stage-by-stage token usage and latency.
• Find bottlenecks, redundant LLM calls, and cost drivers in our clinical RAG workflows.
• Propose and implement optimizations (e.g., advanced caching, prompt compression, state management).
• Document your findings, performance benchmarks, and recommendations for the engineering team.

Requirements

• Proven experience building and optimizing production LLM agents or multi-stage agentic pipelines.
• Strong Python skills and hands-on experience with LangChain and LangGraph.
• Solid understanding of Advanced RAG: medical text embeddings, vector search, semantic chunking, and specialized reranking strategies.
• Experience with OpenAI and Anthropic APIs, including token economics, prompt caching, and strict structured outputs (JSON mode / Pydantic).
• Experience with LLM evaluation and observability tools (e.g., LangSmith, Phoenix, or Arize).
• Ability to explain trade-offs between cost, speed, and clinical safety clearly.

Nice to have

• Experience handling sensitive healthcare data (PHII) and working within HIPAA / GDPR / SOC2 compliance frameworks.
• Experience with Hugging Face models (fine-tuning small medical classifiers, open-source embeddings, or local rerankers).
• Slack API / Slack bot integrations
• Experience integrating AI pipelines with EHR/EMR systems (via HL7, FHIR APIs).

To apply, please include:

1. A short description of an agentic or RAG system you’ve optimized, including the results you achieved (specifically cost, latency, or accuracy numbers).
2. Your experience (if any) handling medical, healthcare, or highly regulated data.
3. Your availability and hourly rate.

We are seeking a Python Machine Learning Developer to build a sentiment classification model using our labeled customer feedback CSV dataset. The work includes reviewing and validating the dataset, cleaning and preprocessing text data, training a suitable model, and evaluating performance on a held-out test set. Deliverables include a trained sentiment classification model, complete reusable Python source code, an evaluation report with accuracy, precision, recall, F1-score, and confusion matrix, saved model and preprocessing components, and a requirements.txt and README with setup and usage instructions.

# ProITBridge | Job Opening: AI Tutor – Cloud Engineering Background

Company: ProITBridge
Engagement Type:Freelance / Part-Time
Time Commitment:Approximately 5 hours per week
Work Mode: Remote

## About ProITBridge
ProITBridge helps learners develop practical skills and prepare for careers in Artificial Intelligence, Generative AI, and Agentic AI through structured learning and personalized mentorship.

## About the Role
ProITBridge is looking for experienced Cloud Engineers who have successfully transitioned into Artificial Intelligence (AI), Generative AI, or Agentic AI, or who have integrated AI technologies into cloud engineering and infrastructure workflows.

The ideal candidate combines professional cloud engineering experience with practical AI knowledge and can guide learners in applying their existing cloud skills to AI-driven careers and solutions.

This role focuses on one-to-one AI mentorship, technical doubt clarification, career transition guidance, and project direction for premium learners. It does not involve building applications on behalf of learners or conducting regular classroom teaching.

## Key Responsibilities
- Conduct one-to-one mentorship sessions with learners transitioning from Cloud Engineering into AI.
- Share practical insights from your journey into AI or your experience integrating AI with cloud technologies.
- Clarify technical doubts related to cloud computing, Machine Learning, Generative AI, LLMs, and Agentic AI.
- Help learners understand how cloud infrastructure, services, and DevOps practices support AI applications.
- Guide learners in selecting suitable AI tools, cloud services, project ideas, and implementation approaches.
- Provide guidance on AI architecture, model deployment, cloud integration, and MLOps practices.
- Review learners' AI projects, proof of concepts (POCs), GitHub repositories, and portfolios, providing constructive feedback.
- Support learners with career transition planning, resume positioning, portfolio development, and interview preparation.
- Identify learners' skill gaps and recommend practical learning paths aligned with their career goals.

## Cloud Engineering Skills
Candidates should have a strong foundation in:
- Cloud Computing: AWS, Microsoft Azure, or Google Cloud Platform (GCP)
- Cloud Infrastructure and Architecture
- Compute, Storage, and Database Services
- Networking Fundamentals
- Identity and Access Management (IAM)
- Docker and Kubernetes
- CI/CD Pipelines
- Infrastructure Automation
- Monitoring and Logging
- Linux
- Git and GitHub
- DevOps Practices

### Artificial Intelligence Skills
Candidates should have practical knowledge of:
- Python for AI
- Machine Learning and Deep Learning Fundamentals
- Generative AI and Large Language Models (LLMs)
- Prompt Engineering
- Embeddings and Vector Databases
- Retrieval-Augmented Generation (RAG)
- AI Tool Calling and Function Calling
- AI Agents and Agentic AI
- LangChain, LangGraph, or Equivalent Frameworks
- AI APIs and Application Integration
- Cloud AI/ML Services
- AI Model Deployment
- MLOps, AI Monitoring, and Model Evaluation

### Professional Requirements
- Previous professional experience in a Cloud Engineering, Cloud Infrastructure, DevOps, or closely related role.
- A demonstrated transition into AI, Generative AI, or Agentic AI, or practical experience integrating AI into cloud engineering workflows.
- The ability to explain technical concepts clearly and simplify complex topics for learners.
- Strong communication, problem-solving, and mentoring skills.
- A genuine interest in guiding professionals who want to transition into AI.

## Preferred Experience
Candidates who have implemented AI solutions using cloud platforms, deployed ML models, integrated LLM applications with cloud services, or worked with AI infrastructure and MLOps workflows are particularly encouraged to apply.

## Time Commitment
This is a flexible, part-time mentorship opportunity requiring approximately 5 hours per week. Candidates can choose either schedule:

- Monday to Friday: 1 hour per day.
- Saturday and Sunday: 2.5 hours per day.

## Why Join ProITBridge?
- Share your career transition journey and help other professionals move into AI.
- Guide learners in applying cloud engineering expertise to real-world AI solutions.
- Contribute to practical AI learning, project development, and career readiness.
- Help professionals understand how cloud computing and AI can work together to build scalable, intelligent systems.

# ProITBridge | Job Opening: AI Mentor – Design Engineering Background

Company:ProITBridge
Engagement Type:Freelance / Part-Time
Time Commitment:Approximately 5 hours per week
Work Mode:Remote

## About ProITBridge
ProITBridge helps learners develop practical skills and prepare for careers in Artificial Intelligence, Generative AI, and Agentic AI through structured learning and mentorship.

## About the Role
ProITBridge is looking for experienced Design Engineers who have successfully transitioned into Artificial Intelligence (AI), Generative AI, or Agentic AI, or who have integrated AI technologies into their design engineering domain.

This opportunity is ideal for professionals who can share their career transition journey, provide practical AI guidance, and help learners understand how to apply AI skills to their existing engineering knowledge and career goals.

The role focuses on 1:1 AI mentorship, technical guidance, and career transition support for premium learners.

## Key Responsibilities
- Conduct one-to-one mentorship sessions with learners exploring AI career opportunities from a Design Engineering background.
- Share practical insights from your transition into AI or your experience integrating AI into Design Engineering.
- Clarify technical doubts related to AI, Machine Learning, Generative AI, and relevant AI tools.
- Guide learners in identifying suitable AI technologies and applications for their engineering domain.
- Provide direction on AI project selection, implementation approaches, architecture, and technology choices when required.
- Review learners’ AI projects, proof of concepts (POCs), GitHub repositories, and portfolios, and provide constructive feedback.
- Support learners with AI career planning, resume positioning, portfolio development, and interview preparation.
- Help learners identify skill gaps and develop a practical roadmap for transitioning into AI-related roles.

## Required Qualifications
- Previous professional experience as a Design Engineer or in a closely related engineering design role.
- A demonstrated transition into AI, Generative AI, or Agentic AI, or practical experience integrating AI into Design Engineering workflows.
- Working knowledge of relevant AI technologies, tools, and applications.
- Practical understanding of AI use cases, project development, and implementation approaches relevant to your experience.
- Strong communication skills and the ability to explain technical concepts clearly.
- A genuine interest in mentoring and supporting professionals transitioning into AI.

## Preferred Technical Skills
Engineering Design & Product Development, CAD / Design Tools, Engineering Drawings & Specifications, Design Analysis & Optimization, Simulation / Modelling, Technical Analysis, Python for AI, Machine Learning & Deep Learning Fundamentals, Computer Vision, Anomaly Detection, AI-Based Design Optimization, Embeddings & Vector Databases, RAG, AI Tool / Function Calling, AI Agents & Agentic AI, LangChain / LangGraph or equivalent frameworks, Generative AI & LLMs, AI APIs & Application Integration or domain-specific AI applications would be valuable.

Candidates with practical examples of how they have applied AI to design processes, engineering workflows, simulation, optimization, or related use cases are particularly encouraged to apply.

## Time Commitment
This is a flexible, part-time mentorship opportunity requiring approximately 5 hours per week.
The schedule can be arranged in either of the following ways:
- Monday to Friday:1 hour per day.
- Saturday and Sunday:2.5 hours per day.

## Why Join ProITBridge?
- Share your professional journey and help others transition into AI.
- Guide learners in applying AI to real-world engineering problems.
- Contribute to practical AI learning, project development, and career readiness.
- Help build a community of engineers exploring AI-driven innovation.

Job Description – Senior / Lead Machine Learning Engineer (AI/ML)

Company: Saradysol Tech Private Limited
Experience: 15+ years; exceptional candidates with equivalent expertise considered
Location: Bengaluru / Remote / Hybrid, based on business requirements
Employment Type: Full-Time

About the Role

We are looking for a hands-on Senior / Lead Machine Learning Engineer who combines strong AI/ML expertise with excellent communication, technical leadership, mentoring skills, and end-to-end ownership.

You will translate business problems into practical AI solutions, design architecture, develop production-ready systems, and guide engineers throughout delivery. The ideal candidate can work independently, make informed decisions, explain complex concepts clearly, and take responsibility for results.

Key Responsibilities

1. End-to-End Ownership and Delivery
- Own AI/ML initiatives from requirement gathering and feasibility assessment through development, deployment, monitoring, and continuous improvement.
- Translate business objectives into clear technical requirements, delivery plans, and measurable success criteria.
- Take responsibility for solution quality, technical decisions, timelines, and production performance.
- Proactively identify risks, dependencies, and blockers, and drive their resolution.
- Communicate progress transparently and escalate issues early with proposed solutions.
2. Machine Learning and Generative AI Development
- Design, develop, evaluate, and optimize machine learning and deep learning solutions.
- Apply appropriate techniques for data preparation, feature engineering, model selection, validation, and performance evaluation.
- Build applications using LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, and agent workflows where appropriate.
- Select suitable approaches based on business needs, data availability, accuracy, latency, cost, and maintainability.
- Establish evaluation baselines, prevent data leakage, and document assumptions and model limitations.
- Assess emerging AI technologies and apply them where they offer practical value.
3. Production Engineering and MLOps
- Write clean, modular, maintainable, and well-tested Python code.
- Build APIs and integrate AI capabilities into enterprise applications.
- Implement reproducible pipelines, experiment tracking, model versioning, automated testing, and CI/CD.
- Deploy and operate solutions using cloud services, Docker, and appropriate MLOps tools.
- Monitor model quality, data drift, reliability, inference latency, and operating costs.
- Troubleshoot production issues and implement sustainable improvements.
- Apply appropriate security, privacy, access-control, and responsible AI practices.
4. Communication and Stakeholder Collaboration
- Explain complex AI/ML concepts clearly to technical and non-technical audiences.
- Lead requirement discussions and ask questions that clarify business needs and expectations.
- Present architecture proposals, evaluation results, technical recommendations, and delivery updates professionally.
- Communicate trade-offs, risks, uncertainty, and limitations openly.
- Write clear technical documentation, architecture explanations, and decision records.
- Collaborate effectively with software engineers, data engineers, DevOps teams, product teams, customers, and management.
5. Technical Leadership, Mentoring, and Teaching
- Provide hands-on technical direction through architecture discussions, code reviews, and problem-solving sessions.
- Mentor junior and mid-level engineers with practical guidance and constructive feedback.
- Explain both how to implement solutions and why specific approaches are appropriate.
- Conduct technical training, knowledge-sharing sessions, and architecture walkthroughs.
- Establish coding standards, engineering practices, and production-readiness expectations.
- Lead through expertise, evidence, and collaboration, including without formal people-management responsibility.
Required Technical Skills
- Programming: Strong Python, SQL, debugging, data structures, and software engineering fundamentals.
- Machine Learning: Supervised and unsupervised learning, feature engineering, model selection, statistical foundations, and evaluation.
- Deep Learning: Hands-on experience with PyTorch or TensorFlow.
- Generative AI: LLM applications, RAG, embeddings, vector search, prompt design, and output evaluation.
- Frameworks: scikit-learn and relevant AI libraries.
- Data Pipelines: Data preprocessing, validation, feature pipelines, and reproducible workflows.
- MLOps: Experiment tracking, model versioning, deployment, and monitoring using MLflow or equivalent.
- Deployment: REST APIs, FastAPI or equivalent, Docker, Git, automated testing, and CI/CD.
- Cloud: Hands-on experience with at least one of AWS, Azure, or GCP.
- System Design: Scalable architecture, observability, reliability, security, and performance optimization.
Essential Professional Qualities
- Excellent communication: Speaks and writes clearly, listens actively, and explains complex topics simply.
- Strong ownership: Drives work to completion and follows through without constant follow-up.
- Technical leadership: Guides engineers, makes sound decisions, and leads by example.
- Teaching mindset: Explains concepts patiently and helps others develop independent problem-solving skills.
- Structured problem-solving: Investigates root causes and evaluates alternatives using evidence.
- Proactive approach: Anticipates challenges and acts before they affect delivery.
- Accountability: Honors commitments and communicates early when priorities or timelines change.
- Business understanding: Connects technical decisions to measurable business outcomes.
- Professional maturity: Accepts feedback and handles disagreements respectfully.
Qualifications and Experience
- Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or equivalent practical experience.
- Demonstrated experience developing, deploying, and supporting production AI/ML systems.
- End-to-end ownership of at least one substantial AI/ML initiative, with clear evidence of individual contribution.
- Experience making architecture decisions and explaining technical and business trade-offs.
- Evidence of mentoring engineers, leading technical initiatives, or conducting technical training.
- Ability to work independently while collaborating effectively across teams.

Job Description

We are looking for a highly experienced Principal AI Architect / LLM Specialist to provide strategic and technical consultation for our AI initiatives.

We are building AI solutions for enterprise and government environments, including fully offline (air-gapped) deployments where external cloud services and APIs are not permitted.

We need an expert with strong hands-on experience in deploying, training, fine-tuning, evaluating, and optimizing different Large Language Models (LLMs).

Our primary objective is to validate that our technical architecture, LLM selection, infrastructure, and development approach are moving in the right direction.

This is primarily a consultancy and architecture advisory engagement, not a full-time development role.

Key Responsibilities

Review our current AI architecture, infrastructure, and technical roadmap.

Recommend the most suitable open-weight LLMs for different business use cases.

Guide the deployment and configuration of fully offline, on-premises LLM environments.

Advise on LLM fine-tuning using LoRA, QLoRA, and other appropriate training techniques.

Review model optimization strategies, including quantization, inference acceleration, memory management, and GPU utilization.

Assess hardware requirements, especially GPU VRAM, system RAM, storage, and performance limitations.

Recommend approaches for building multi-model and agentic AI architectures.

Evaluate trade-offs between model accuracy, performance, infrastructure cost, and scalability.

Conduct technical review sessions with our AI development team and identify architectural risks or gaps.

Required Hands-On Expertise

1. Offline / Air-Gapped LLM Deployment

Practical experience deploying models without internet connectivity or external APIs.

Experience with local inference frameworks such as Ollama, llama.cpp, vLLM, or equivalent technologies.

Experience with GPU optimization and deployment on local servers and workstations.

2. LLM Training and Optimization

Fine-tuning open-weight LLMs using LoRA / QLoRA.

Dataset preparation, model evaluation, and benchmarking.

Quantization techniques (4-bit, 8-bit, etc.).

Optimizing inference latency, throughput, and memory usage.

Understanding when to use RAG, fine-tuning, or pretraining.

3. Experience with Different LLM Categories

We expect practical experience selecting, evaluating, and optimizing models for multiple applications, including:

Reporting & Analytics: Generating business reports, summaries, insights, and structured outputs from enterprise data.

Coding & Code Generation: Code generation, code explanation, debugging, code review, and development assistance.

Reasoning & Decision Support: Complex reasoning, analysis, and multi-step problem-solving.

Document Intelligence: Extracting and analyzing information from PDFs, technical documentation, and enterprise knowledge bases.

AI Agents & Automation: Tool-using agents, workflow automation, and multi-model orchestration.

4. AI Architecture & Security

Enterprise AI solution architecture.

Secure, isolated, on-premises deployment.

RAG pipelines, vector databases, and knowledge retrieval.

Data privacy and secure AI infrastructure.

Model evaluation, monitoring, and governance.

Expected Deliverables

Architecture Assessment: Review of our current setup and identification of technical gaps and risks.

LLM Recommendation Matrix: Recommended models for reporting, coding, reasoning, document processing, and automation.

Infrastructure Assessment: Hardware suitability, GPU/VRAM requirements, and optimization opportunities.

Technical Roadmap: Recommended architecture and implementation priorities.

Knowledge Transfer: Technical consultation sessions with our development team, including practical deployment and optimization guidance.

Ideal Candidate

8+ years of software/AI engineering experience, with significant recent hands-on LLM experience.

Proven experience deploying LLMs fully offline or on-premises.

Strong background in model fine-tuning and inference optimization.

Practical experience working with multiple open-weight model families.

Previous experience designing enterprise-grade AI architectures.

Ability to challenge technical decisions and provide independent, evidence-based recommendations.

Important: We are not looking for someone whose experience is limited to ChatGPT API integrations, prompt engineering, or cloud AI services. We need a genuinely hands-on LLM architect who understands model deployment, customization, hardware constraints, and optimization.

3D Garment & Cloth Simulation Engineer: Build a Fully Dynamic Indian Blouse Draping Pipeline

About the Project

We are building a platform that transforms a customer's blouse design into a realistic 3D garment fitted to their individual body shape and measurements.

We have already developed a functional pipeline for generating authentic Indian saree blouses. Our next goal is to make this pipeline fully dynamic, scalable, and data-driven, enabling it to support new designs, styles, and measurement variations automatically, without requiring developers to write custom rules or manually configure every garment style.

We are looking for an experienced 3D garment simulation engineer who can help us develop a generalized, production-ready solution for procedural garment construction, sewing pattern generation, and realistic cloth simulation.

What We Have Built So Far

Our existing system includes the following components:
- FastAPI-based garment generation service: Accepts SMPL-X-based body meshes in T-pose and A-pose, along with SVG designs for the blouse front, back, and sleeves, and generates a draped 3D blouse in GLB format in approximately 30–70 seconds.
- Blender-based cloth simulation: Includes automated body measurement extraction, garment panel scaling, seam detection, sleeve fitting, and texture transfer.
- Garment style catalogue: Supports 35 neckline variations, 11 sleeve styles, and multiple blouse types, including closed keyhole designs with 3D buttons.
- Early-stage procedural pattern generation: Generates initial sewing patterns using style codes and customer measurements, including neckline depth, blouse length, shoulder width, bust, waist, hem, and sleeve length.
- Web-based design studio: Allows designers to create blouse designs using SVGs and customize garment measurements through an interactive interface.
Current Challenges

Although the existing pipeline works for supported designs, several limitations prevent it from scaling to arbitrary garment styles.
1. Limited generalization: New neckline shapes, sleeve constructions, and blouse styles still require manually written rules and custom implementation.
2. SVG-to-pattern conversion: The SVG designs represent the visual appearance of the blouse but do not necessarily contain the geometric information required to generate accurate, sewable 2D pattern pieces. This conversion is only partially automated.
3. Complex garment construction: Multi-layer designs, including tulip sleeves, petal sleeves, tiered constructions, linings, collars, keyholes, and different closure mechanisms, require a more generalized construction system.
4. Measurement-driven adaptability: Changes to customer measurements must automatically update pattern geometry while preserving seam compatibility, garment proportions, and construction constraints.
5. Decoration alignment: Zari borders, embroidery, stonework, and other decorative elements must remain correctly positioned along necklines, sleeve edges, and garment boundaries when the pattern geometry changes.
6. Simulation performance and reliability: The pipeline must consistently produce realistic garment draping across different body shapes and blouse designs within the target processing time.
What We Want You to Build

1. Generalized SVG-to-Sewing-Pattern Generation

Develop a system that converts designer-provided SVGs, style metadata, construction tags, and customer measurements into geometrically valid, sewable 2D pattern pieces.

The system should identify or infer garment panels, seam boundaries, darts, folds, notches, and other relevant construction features. It should support named edges and seam relationships so that compatible pieces can be assembled and simulated automatically.

The solution should generalize to new designs rather than depend on hard-coded rules for individual styles.

2. Parametric Bodice and Sleeve Construction

Build a reusable, measurement-driven pattern system for blouse bodices and sleeves.

The system should automatically adapt to changes in bust, waist, shoulder, blouse length, neckline depth, armhole dimensions, and sleeve measurements while maintaining matching seam lengths, appropriate ease, and valid garment construction.

It should support reliable sleeve-cap and armhole matching, dart placement, and other essential pattern-drafting constraints.

3. Data-Driven Support for Complex Garment Structures

Implement a flexible construction framework for multi-component garments, including:
- Layered and overlapping sleeve panels, such as tulip and petal sleeves.
- Tiered constructions and multiple fabric layers.
- Linings, collars, and facing components.
- Keyholes, button closures, and related garment details.
- Configurable seam relationships and assembly instructions.
New construction types should be definable through structured data, configuration, or reusable procedural components wherever possible, rather than requiring custom code for every new style.

4. Parametric Decoration and Texture Mapping

Develop a decoration-mapping system that keeps prints, zari borders, embroidery, stonework, and other ornamental details aligned with the garment geometry.

Decorations should follow changes to pattern dimensions, neckline contours, sleeve shapes, and garment edges without requiring manual repositioning after every measurement or design update.

5. Reliable and Efficient 3D Draping

Improve the existing Blender-based simulation pipeline to support diverse body shapes, garment measurements, and construction types.

The target is to generate a realistic, properly fitted blouse in under 60 seconds per garment while maintaining garment integrity, seam alignment, and consistent output quality.

6. Integration, Testing, and Documentation

Integrate the solution into our existing repository and FastAPI service.

Deliver clean, modular, maintainable Python code, along with documented APIs, configuration examples, automated tests, and representative test cases covering different blouse styles, body measurements, and garment construction scenarios.

Required Technical Skills
- Garment construction and pattern drafting: Strong knowledge of 2D sewing patterns, bodice blocks, darts, seam allowances, neckline construction, sleeve-cap geometry, armhole matching, and garment assembly.
- 3D garment simulation: Practical experience with Blender Python (bpy) and cloth simulation, or equivalent technologies such as CLO3D, Marvelous Designer, GarmentCode, or NVIDIA NvCloth.
- Python development: Experience building procedural geometry systems, modular software components, and production-ready pipelines.
- Computational geometry: Familiarity with meshes, triangulation, polygon operations, SVG parsing, Bézier curves, geometric constraints, and mesh processing.
- Body models and fitting: Experience with SMPL, SMPL-X, or similar parametric human body models, including garment fitting and body-shape adaptation.
Preferred Qualifications
- Experience with Indian ethnic wear, particularly saree blouses, traditional necklines, decorative borders, and varied sleeve constructions.
- Experience developing parametric garment systems or procedural sewing-pattern generation tools.
- Familiarity with FastAPI, Docker, and Google Cloud Platform (GCP).
- Knowledge of computational pattern drafting, garment CAD, and research on automated garment generation.
- Experience optimizing cloth simulation for production workloads.
Expected Deliverables

The successful candidate will be expected to deliver:
1. A generalized SVG-to-sewing-pattern generation pipeline.
2. Reusable, measurement-driven bodice and sleeve pattern components.
3. Data-driven support for layered garments, collars, closures, and complex sleeve constructions.
4. Parametric decoration and texture mapping.
5. An optimized garment simulation pipeline integrated with our existing FastAPI service.
6. Documented source code, automated tests, and sample outputs demonstrating support for previously unseen designs.
How to Apply

Please include the following information in your application:
- Relevant experience: Links to projects, repositories, videos, or demonstrations of garment simulation, procedural garment generation, or sewing-pattern automation.
- Technical approach: Explain how you would convert a visual SVG design into geometrically valid, sewable 2D pattern pieces, including how you would handle seam matching and complex garment constructions.
- Generalization strategy: Describe how you would support new blouse styles and measurement variations without introducing hard-coded rules for each design.
- Availability and rates: Share your availability, estimated project timeline, and expected hourly or fixed-project rate.
We will share our existing codebase, technical documentation, and sample designs with shortlisted candidates.

Our objective is to move beyond a style-specific garment generation pipeline and build a generalized, scalable system that can automatically transform new blouse designs into accurate sewing patterns and realistic 3D garments.

We are seeking a skilled professional to support AI, machine learning, and deep learning projects while also contributing to website development and data science tasks. The ideal candidate should be able to handle both technical and creative aspects of the work, including building and improving websites, analyzing data, and supporting AI-driven solutions. If you have experience working across these areas and can help deliver high-quality results, we would love to hear from you.

We are ASFH (AI Solutions for Healthcare), a Saudi-based healthcare AI startup founded by a medical doctor and an AI Software Engineer.

We are looking to hire an experienced AI Software Engineer to help develop and enhance Wahab, our existing AI-powered medical assistant.

We already have a working AI agent and an established codebase. We are not looking to build a chatbot from scratch, but rather to improve, expand, and transform our existing system into a scalable, production-ready medical AI platform.

Key Responsibilities:

* Enhance existing LLM agents and agentic AI workflows.
* Improve RAG pipelines, medical knowledge retrieval, and source accuracy.
* Implement advanced tool calling, orchestration, and persistent memory.
* Develop and integrate backend APIs and a modern chat interface.
* Support multimodal capabilities, including future medical computer vision integration.
* Improve system performance, reliability, security, and scalability.
* Collaborate directly with our technical lead.

Required Skills:

* Python and FastAPI
* LangGraph / LangChain
* LLMs, RAG, and Agentic AI
* AI agent architecture and tool calling
* Full-stack development (React / Next.js preferred)
* API integration and database management
* Docker, Git, and production deployment

Preferred Experience:

* Healthcare AI applications
* Medical RAG and evidence-based retrieval
* Multimodal AI and computer vision integration
* LLM evaluation, guardrails, and data privacy

Engagement: Remote, paid freelance position (hourly or milestone-based).

We’re looking for someone who can work with our existing codebase, collaborate with our founding technical team, and contribute to the long-term development of the platform.

To Apply: Please share relevant AI agent projects you’ve developed, your technical stack, hourly rate, and availability.

We’re hiring a Research Signal Processing Engineer to explore non-invasive glucose estimation using facial photoplethysmography from standard smartphone cameras.

This role is focused specifically on the feasibility, signal extraction, feature engineering, and modeling challenges associated with non-invasive glucose estimation from facial video.

Preferred experience

facial photoplethysmography, biomedical signal processing, or physiological sensing

Investigate glucose-sensitive physiological features and signal patterns

Evaluate spectral, temporal, vascular, and multimodal features

Glucose estimation, spectroscopy, diabetes research, or metabolic biomarkers is a plus

Publications or prior R&D in non-invasive sensing are highly valued

Role Summary
Looking for an experienced AI/ML Architect to lead our AI/ML architecture, agentic AI
platform design, and applied AI research initiatives for healthcare payer operations.
This is a senior technical leadership role for someone who can combine hands-on AI/ML
expertise, healthcare payer understanding, architecture depth, and research leadership to
help build next-generation AI solutions for utilization management, care management,
claims, provider operations, pharmacy, and member engagement.
Role Overview
The AI/ML Architect will lead the design and evolution of organization's AI/ML capabilities,
including machine learning models, payer-specific LLMs, retrieval-augmented generation,
intelligent agents, and agentic AI workflows. This role will also lead company’s AI/ML
Research Frontier Team, focused on exploring emerging AI methods and translating them
into scalable, production-ready payer solutions.
Key Responsibilities
AI/ML Architecture Leadership
• Define company’s AI/ML architecture roadmap across products and platforms.
• Design scalable architectures for ML models, LLM applications, RAG pipelines, and
agentic workflows.
• Establish reusable patterns for model development, inference, evaluation,
monitoring, and governance.
• Ensure AI/ML solutions are secure, modular, explainable, and deployable within
payer environments.
Agentic AI & Workflow Design
• Architect modular AI agents for payer workflows such as prior authorization,
medical necessity review, policy summarization, risk prediction, care management,
provider service, and member service.
• Design multi-agent workflows across UM, CM, claims, pharmacy, provider, and
member operations.
• Guide the evolution of company’s Agentic Operating Platform for cross-domain
orchestration.
AI/ML Research Frontier Leadership
• Lead research workstreams in LLMs, agentic AI, clinical NLP, graph intelligence,
predictive modeling, digital twins, multimodal AI, and synthetic data.
• Evaluate emerging AI techniques and convert promising research into product-ready
capabilities.
• Partner with academic, research, and engineering teams to accelerate innovation.
Technical Leadership & Governance
• Mentor AI/ML engineers, data scientists, data engineers, and application teams.
• Review architecture, model design, implementation plans, and technical standards.
• Establish MLOps and LLMOps practices for CI/CD, model registry, evaluation,
observability, and monitoring.
• Support secure cloud or payer-environment deployment across Azure, AWS, or
client-managed infrastructure.
Required Experience
• Hands-on Experience in software engineering, data science, AI/ML, or enterprise
architecture.
• Hands-on Experience in designing and deploying AI/ML solutions in production.
• Strong hands-on experience with ML, deep learning, NLP, LLMs, RAG, and AI
application architecture.
• Experience with Python and frameworks such as PyTorch, TensorFlow, Scikit-learn,
Hugging Face, LangChain, LlamaIndex, or Semantic Kernel.
• Experience with vector databases and search technologies such as pgvector,
Qdrant, Weaviate, Milvus, OpenSearch, or Elasticsearch.
• Strong understanding of MLOps, LLMOps, model monitoring, AI governance, and
responsible AI practices.
• Ability to communicate complex AI concepts to executives, product teams,
engineers, and healthcare stakeholders.
Preferred Experience
• Experience leading research teams or translating AI research into commercial products.

LungLens AI — Explainable Multi-Disease Chest X-Ray Analysis Platform is an AI-powered research and educational web application designed to analyze chest X-ray images using a pretrained DenseNet-121 deep learning model. Its purpose is to explore multiple potential lung and chest abnormalities through multi-label model predictions, provide visual explanations using Grad-CAM heatmaps, and generate downloadable PDF reports containing the image, model scores, and analysis metadata. The application also includes image validation to reject unsuitable inputs before inference, a modern interactive dashboard, image preprocessing, error handling, and model information. The project deliverables include a working web demo, complete Python source code, pretrained model integration, image validation pipeline, Grad-CAM explainability module, downloadable PDF report generation, automated tests, README with setup instructions, project report, and genuine application screenshots. The system is intended for educational and research purposes only, not for clinical diagnosis or medical decision-making.

Skills: Python, Machine Learning (ML), Image Processing, Web Development, Computer Vision, Deep Learning, Convolutional Neural Network

I’m building a web-based store where shoppers can choose a dress or suit and instantly see it on themselves. The flow is simple: the visitor uploads one full-body, front-facing photo, picks an item from the catalogue, and the site renders a realistic composite that respects body shape, pose and lighting.

The stack is already defined—Python with Flask on the back end, HTML/CSS/JavaScript on the front, and a deep-learning pipeline (pose estimation, segmentation, texture warping) driving the virtual try-on. What I need now is an end-to-end implementation that feels production ready: quick inference times, clean UX, mobile responsiveness, and an admin view for adding garments.

If you’re interested, send a detailed project proposal outlining the approach you’d take, libraries or pre-trained models you’d rely on, and a rough milestone breakdown so I can understand how you plan to hit accuracy, speed, and visual-quality targets.

Key deliverables
• Flask application with user sign-up/login, secure image upload, and outfit selection
• AI module that composites full outfits (dresses and suits first) onto the user photo with minimal artefacts
• Responsive storefront UI plus simple admin dashboard
• Deployment scripts or Dockerfile so I can spin it up on a standard cloud VM

I’ll review proposals this week and shortlist the ones that demonstrate solid computer-vision insight and pragmatic e-commerce thinking. Looking forward to seeing how you’d bring this experience to life.

Skills: JavaScript, Python, CSS, HTML, Web Development, Flask, Computer Vision, Deep Learning

AI Video Creator for Realistic Preschool Learning Videos**

I’m looking for an experienced AI video creator to produce **two 30-minute educational videos** for kids. I'll provide the written scripts.

The videos should use realistic human characters and everyday situations to practice:

- Simple conversations, asking questions, and telling short stories.
- Understanding yes and no and expressing likes and dislikes.
- Asking for help and offering help.
- Finding objects and explaining where things are.
- Recognizing familiar people and describing their actions.
- Choosing clothes and food.
- Taking turns, using safe hands, and expressing frustration calmly.
- Following daily routines, including putting shoes away and washing hands.

**Requirements**

- Consistent, realistic characters throughout both videos.
- Natural English voices, clear pronunciation, and accurate lip-sync.
- Gentle pacing, simple backgrounds, and minimal distractions.
- Short chapters with pauses for responses and parent-child activities.
- Final delivery in 1080p MP4, with captions and a version without captions.
- Properly licensed visuals, voices, and music.

**Please include in your application:**

- Relevant examples of realistic AI videos you have created.
- The tools you plan to use.
- Your estimated delivery timeline and included revisions.
- Any experience creating educational content for preschool children.

The project will begin with a short sample scene to establish the characters, voices, and visual style before full production.