What You''ll Do
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 Tamil Nadu (India)
🏢 Mesitus
📍 India