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 ok