Agentic AI

Actively Reviewing the Applications

HuntingCube

4–8 years

Posted 3 days ago • Apply by June 11, 2026

Job Description

Job Description

Job Description – Agentic AI Engineer (4–6 yrs)

Location: Bangalore, Experience: 4 to 6 years

Employment Type: Permanent

Company: KPMG

Role Summary

KPMG is hiring an Agentic AI Engineer with handson experience in multiagent systems, GenAI architectures, RAG pipelines, and LLM-based automation. The role focuses on designing scalable AI workflows, tooldriven execution, and retrievaloptimized solutions for enterprise use cases.

Key Responsibilities

Build agentic AI workflows using frameworks such as LangChain, LangGraph, AutoGen, Haystack, and MCP.
Design end-to-end GenAI architectures with multi-agent orchestration, tools, memory, and RAG.
Develop prompts, manage context, and implement retrieval strategies to improve LLM accuracy.
Implement tool-calling flows and coordinate multi-agent task execution.
Build robust RAG pipelines leveraging vector search, chunking, embeddings, and LLM integration.
Work with vector databases to enable efficient semantic retrieval.
Develop Python-based data pipelines and write performant SQL for analytics and ETL.

Mandatory Skills (Required Experience)

Handson experience with Agentic AI frameworks: LangChain, LangGraph, AutoGen, Haystack, MCP
Experience in End-to-end GenAI architecture design (multi agent, tools, memory, RAG)
Minimum 1.5 to 2 years of relevant experience in building Gen AI Apps and multiagent workflows
Good knowledge in Prompt engineering, context management, retrieval strategies
Experience in Tool-calling and multi-agent orchestration
Strong expertise in RAG design: vector search, chunking, LLM, embeddings
Practical experience with Vector DBs: Qdrant, Weaviate, PGVector, Chroma
Strong experience with SQL and Python (ETL, data pipelines)

Required Skills

['Agentic Ai']

Additional Information

Mandatory Skills (Required Experience)

Handson experience with Agentic AI frameworks: LangChain, LangGraph, AutoGen, Haystack, MCP
Experience in End-to-end GenAI architecture design (multi agent, tools, memory, RAG)
Minimum 1.5 to 2 years of relevant experience in building Gen AI Apps and multiagent workflows
Good knowledge in Prompt engineering, context management, retrieval strategies
Experience in Tool-calling and multi-agent orchestration
Strong expertise in RAG design: vector search, chunking, LLM, embeddings
Practical experience with Vector DBs: Qdrant, Weaviate, PGVector, Chroma
Strong experience with SQL and Python (ETL, data pipelines)