AI Engineer (RAG & Agentic Systems)

About the Role

We are looking for a hands-on AI Engineer to lead the development of an Agentic RAG (Retrieval-Augmented Generation) system. This is a greenfield AI initiative focused on building an intelligent knowledge assistant for healthcare credentialing workflows, with a roadmap from knowledge Q&A through to automated agentic actions.

You will work closely with the AI Project Lead and collaborate with an existing MERN stack development team to build reliable, production-grade AI capabilities.

Key Responsibilities

• Design, develop, and maintain Retrieval-Augmented Generation (RAG) systems, including embedding pipelines, vector indexing, and retrieval optimization using LangChain and LangGraph.
• Build and maintain knowledge-ingestion pipelines covering URL scraping, PDF/DOCX processing, chunking, embedding generation, and vector database indexing.
• Integrate and optimize Large Language Models through AWS Bedrock, including model selection, prompt engineering, and cost/latency optimization.
• Implement agentic workflows using LangGraph for multi-step reasoning, tool orchestration, and autonomous task handling.
• Build and expose REST APIs using FastAPI or Flask to connect the AI backend with React-based interfaces and chat applications.
• Implement AI monitoring frameworks covering logging, hallucination detection, response-quality evaluation, and performance tracking.
• Collaborate with the MERN stack team on frontend chat integration and knowledge-management interfaces.
• Maintain clean, well-documented, production-grade code and containerized deployments using Docker on AWS.

Required Skills & Experience

• Strong Python skills; Python is the primary development language for this role.
• Hands-on experience with LangChain and/or LangGraph for RAG or agentic pipelines.
• AWS experience, particularly with Bedrock, S3, and EC2; familiarity with IAM and VPC is an advantage.
• Practical experience with at least one vector database such as pgvector, Pinecone, or Weaviate.
• Experience building AI backend services and REST APIs using FastAPI or Flask.
• Hands-on experience with Docker and containerized application deployment.
• Comfortable working with SQL databases and relational data.
• Good understanding of embeddings, semantic search, and chunking strategies for RAG systems.

Good to Have

• React or basic JavaScript knowledge for frontend coordination.
• Experience with Terraform or other infrastructure-as-code tools.
• Familiarity with AI governance, hallucination detection, or response-evaluation frameworks.
• Exposure to healthcare or other compliance-heavy domains.
• Personal or open-source RAG/LLM projects on GitHub are highly valued.

What We Are Looking For

Demonstrated hands-on capability matters more than certifications alone. Candidates who have independently built RAG or LLM-based projects, including personal or open-source work, are encouraged to share GitHub repositories or project demonstrations during the interview process.

CTC: ₹8-12 LPA

Experience: 2-4 Years