GenAI / Agentic AI Engineer

5–10 Years | GenAI + Agentic + LangChain Ecosystem+ Classical ML                                                                                                                        

Overview

This role demand strong GenAI experience, emerging mastery in Agentic AI Systems, and a good foundation in classical ML.

You will design and build intelligent, tool-using agents, multi-agent systems, RAG pipelines, and LLM-based applications leveraging the LangChain , LangGraph ecosystem, LangSmith for evaluation.

Key Responsibilities

1. GenAI / LLM Application Development

• Build GenAI applications using:
• LangChain, LangGraph
• Implement RAG architectures with:
• Retrieval, reranking, chunking, memory strategies
• Vector DBs (faiss, aisearch, opensearch, PG vector etc).
• Design prompt-engineering strategies:
• Instruction-following
• ReAct (Reasoning + Acting)
• Chain-of-thought structuring
• Self-reflection and planning loops
• Evaluation Strategy

o  Implement evaluation frameworks for Classical ML and GenAI systems, covering statistical validation, reliability, and robustness.

o  Assess LLM outputs, RAG pipelines, and agent workflows for grounding quality, relevance, and retrieval accuracy (e.g., recall@k, precision@k).

o  Use LangSmith for tracing, automated evaluations, regression testing, and continuous system‑level quality monitoring

2. Agentic System Architecture

• Build agentic workflows:
• Tool-calling agents
• Planner–executor systems
• Multi-agent communication systems
• Hierarchical agent architectures
• Deep Agents
• Integrate memory systems:
• episodic memory
• semantic memory
• vector-based long-term knowledge
• Implement evaluation frameworks for agentic systems using LangSmith.

3. Model Context Protocol (MCP) & Tooling

• Implement MCP servers for external tool connectivity.
• Build tools that allow agents to interact with:
• APIs
• Code execution environments
• Knowledge bases
• Company applications

4. Classical ML (Foundational DS Skills)

• Apply ML models to structured/unstructured data.
• Conduct feature engineering, model selection, hyperparameter tuning.
• Build interpretable models where required.

5. Engineering & Integration

• Collaborate with backend engineering teams to seamlessly integrate agentic and GenAI systems into production applications.
• Implement observability, tracing, and monitoring for GenAI workflows using LangSmith to ensure reliability and system‑level transparency.

6. Cloud ML-Ops & Quality

• ML Modelling, data drift, concept drift, model quality monitoring.
• Hands‑on experience across AWS/ Azure/ Databricks, with flexibility to work on any cloud platform.

·       Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown)