GenAI Developer

Key Responsibilities

• Design, develop, and deploy enterprise Generative AI applications using Azure OpenAI Services. 
• Build Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases. 
• Develop scalable REST APIs using FastAPI and Flask. 
• Integrate Vision LLMs for image, document, and multimodal understanding. 
• Build document processing pipelines using PyMuPDF for PDF extraction, parsing, and preprocessing. 
• Implement semantic search using FAISS Vector Database. 
• Engineer prompts and optimize LLM responses for enterprise use cases. 
• Develop AI-powered chatbots, document Q&A, summarization, and intelligent automation solutions. 
• Optimize AI models for latency, scalability, and cost efficiency. 
• Integrate AI solutions with enterprise applications and cloud services. 
• Implement monitoring, evaluation, and experimentation frameworks using Opik or similar LLM observability tools. 
• Collaborate with product managers, architects, data scientists, and software engineers to deliver AI solutions. 
• Ensure AI applications follow security, governance, and responsible AI best practices. 

Required Skills

Generative AI

• Large Language Models (LLMs) 
• Prompt Engineering 
• Retrieval-Augmented Generation (RAG) 
• Embeddings 
• Semantic Search 
• AI Agents 
• Function Calling 
• Context Management 
• Model Evaluation 

Cloud & AI Platforms

• Azure OpenAI Service 
• Azure AI Services 
• Azure Cognitive Search (preferred) 
• Azure Storage 
• Azure Functions (preferred) 

Programming

• Python (Advanced) 
• FastAPI 
• Flask 
• REST API Development 
• Async Programming 

AI Frameworks & Libraries

• LangChain 
• LlamaIndex 
• PyMuPDF 
• FAISS Vector Database 
• Vision LLMs 
• OpenAI SDK 
• Transformers (preferred) 

Development Tools

• Visual Studio Code (VS Code) 
• PyCharm 
• Git 
• GitHub/Azure DevOps 
• Docker 

Observability & Evaluation

• Opik 
• Prompt evaluation 
• LLM monitoring 
• Experiment tracking 
• Performance benchmarking 

Required Experience

• 5–10 years of software development experience with strong Python expertise. 
• Minimum 2–4 years of hands-on experience in Generative AI and LLM-based application development. 
• Experience implementing enterprise RAG architectures. 
• Strong experience with Azure OpenAI. 
• Experience integrating Vision LLMs for document and image processing. 
• Hands-on experience with vector databases such as FAISS. 
• Experience building production-ready AI APIs using FastAPI or Flask. 
• Experience processing large PDF/document repositories using PyMuPDF. 
• Experience with AI evaluation and observability tools such as Opik.  
• Experience deploying AI applications in cloud environments. 

Nice-to-Have Skills

• LangGraph 
• AutoGen/CrewAI 
• Azure AI Search 
• Cosmos DB 
• PostgreSQL 
• Redis 
• Kubernetes 
• MLflow 
• Hugging Face 
• OCR (Azure Document Intelligence, Tesseract) 
• CI/CD pipelines 
• MLOps

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