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

Overview

This role demand solid 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)

📌 GenAI / Agentic AI Engineer (Bengaluru)
🏢 Vergil Remnant Consultant Services Private
📍 Bengaluru