AI Engineer(Agentic AI )

AI Engineer(Agentic AI )

We are looking for candidates with experience in:

• Generative AI, Agentic AI, and LLM-based applications
• RAG architectures, embeddings, semantic search, and vector databases
• LangChain or similar AI orchestration frameworks
• Document parsing, chunking strategies, and unstructured data processing
• Designing and deploying AI-powered enterprise applications
• FastAPI and API development
• AWS cloud services
• MLOps / DevOps practices
• Data pipelines, data engineering, and metadata management (DataHub experience is a plus)
• End-to-end solution architecture and production deployment of AI solutions

Based on both the JD and the interview feedback, the client is not looking for a traditional Data Scientist who has some Agentic AI exposure. They are looking for someone whose core identity is an AI Engineer/Agentic AI Engineer.

What the client probably wants

Primary Experience in Agentic AI

Someone who has spent a significant portion of their recent career building:

• AI Agents
• Multi-Agent Systems
• Autonomous Workflows
• Agent Orchestration
• LLM-native Applications
• AI Copilots and Assistants

rather than someone who started as a Data Scientist and later picked up Agentic AI.

Strong LLM Engineering

They likely expect hands-on experience with:

• LangGraph
• LangChain
• CrewAI
• AutoGen
• MCP
• AI Agent frameworks
• Function Calling
• Tool Calling
• Memory Management
• Agent Evaluation

as day-to-day responsibilities rather than project exposure.

Enterprise GenAI Product Development

The JD emphasizes:

• Enterprise AI applications
• RAG
• Semantic Search
• Vector Databases
• Production Deployment
• Solution Architecture