Gen AI Engineer RAG, Agentic AI & End-to-End Deployment

We are looking for a hands-on Gen AI Engineer who can design, build, deploy, and continuously improve production-grade AI solutions. The ideal candidate should have strong experience across RAG, AI Agents, LLMs, vector databases, Python, FastAPI, cloud deployment, and MLOps/LLMOps.

Key Responsibilities :

- Design and build production-grade RAG pipelines, including document ingestion, preprocessing, chunking, metadata enrichment, embeddings, vector search, retrieval, and re-ranking.

- Develop strategies to improve retrieval quality, relevance, grounding, and response accuracy.

- Design and implement single-agent and multi-agent AI systems capable of reasoning, tool usage, planning, and task execution.

- Own the complete AI solution lifecycle : data pipeline, RAG/agent development, evaluation, deployment, monitoring, and optimization.

- Build scalable APIs and backend services using Python and FastAPI.

- Deploy AI applications in cloud or on-premise environments with a focus on scalability, reliability, security, and maintainability.

- Build and maintain CI/CD pipelines with automated testing and evaluation of AI/agent outputs.

- Implement monitoring, logging, and observability for latency, cost, accuracy, quality, drift, failures, and user feedback.

Tech Stack & Requirements :

- Strong hands-on experience with Python and FastAPI.

- Deep expertise in RAG (Retrieval-Augmented Generation).

- Vector databases : Pinecone, Weaviate, FAISS, Chroma, or pgvector.

- AI Agent frameworks : LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.

- Experience with Docker, Kubernetes, and cloud platforms (AWS/Azure/GCP).

- Experience deploying at least one AI solution into a production environment.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Engineering, AI/ML, or a related field.

- 3-8+ years of experience in AI/ML engineering or software engineering with strong Python development.