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
