AI/ML Engineer (VIT/HR/AIMLE/06/0726)

Experience: - 6–10+ years in software engineering - 4+ years in GenAI / NLP / LLM-based systems - Proven experience building production-grade AI systems Key Responsibilities - Design and build RAG pipelines (ingestion → indexing → retrieval → generation) - Develop data ingestion pipelines: - Document upload (PDF, DOCX, PPT) - Parsing, table extraction, image extraction - Image captioning & semantic chunking - Metadata tagging & versioning - Implement hybrid search (vector + keyword + optional graph) - Build APIs using FastAPI (Python) for chat and orchestration - Integrate LLMs (OpenAI / Claude via AWS Bedrock) - Optimize response accuracy, latency, and cost - Implement evaluation & observability (RAGAS, Langfuse/LangSmith) Tech Stack - Languages/Backend: Python, FastAPI - LLM & Frameworks: LangChain, LangGraph, CrewAI - Cloud: AWS (S3, ECS/Fargate, Lambda, API Gateway, Bedrock) - Databases: Any Vector DB - Data Ingestion: Unstructured, PyMuPDF, Tesseract, custom pipelines - DevOps: Docker, GitHub Actions - Observability/Eval: Langfuse, LangSmith, RAGAS, DeepEval Requirements - 6+ years in software engineering, strong in Python - Hands-on experience with LLMs & RAG systems - Experience building data ingestion pipelines and search systems - Good understanding of embeddings, semantic search, and prompt engineering - Experience with cloud deployment (AWS preferred) Nice to Have - Multi-modal RAG (images, tables) - Agentic AI workflows (LangGraph / ReAct patterns) - Experience with enterprise AI systems