- Design and build Retrieval-Augmented Generation (RAG) pipelines - chunking, embeddings, semantic search, and retrieval tuning for accuracy and grounding.
- Develop AI agents and multi-step workflows using LangChain / LangGraph, including tool calling and orchestration across multiple steps.
- Build and maintain backend services and REST APIs (FastAPI / Flask) that expose AI capabilities to internal and member-facing applications.
- Integrate and optimise usage of LLM providers (OpenAI, Anthropic, Gemini), balancing quality, latency, and cost.
- Work with vector databases (Pinecone, Weaviate, Chroma, FAISS, or Qdrant) and relational/document stores (PostgreSQL / MongoDB) for storage and retrieval.
- Apply robust prompt engineering practices and iterate based on evaluation results.
- Containerise and deploy services with Docker, and contribute to Git-based CI/CD workflows.
- Set up evaluation and quality checks for AI outputs, and continuously improve reliability and accuracy.
📌 GenAI & Agentic System Engineer (Chennai)
🏢 AVE-Promagne
📍 Chennai
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