Posted 20 May, 2026
DEQ - Manager - AIML
KPMG
Bangalore,Karnataka,IN,560071
Full Time
Reference: 218_609639_INTG10044651
About KPMG in India
KPMG entities in India are professional services firm(s). These Indian member firms are affiliated with KPMG International Limited. KPMG was established in India in August 1993. Our professionals leverage the global network of firms, and are conversant with local laws, regulations, markets and competition. KPMG has offices across India in Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Jaipur, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara and Vijayawada.
KPMG entities in India offer services to national and international clients in India across sectors. We strive to provide rapid, performance-based, industry-focused and technology-enabled services, which reflect a shared knowledge of global and local industries and our experience of the Indian business environment.
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.
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Key Responsibilities
1. GenAI / LLM Application Development
Build GenAI applications using:
o LangChain, LangGraph
Implement RAG architectures with:
o Retrieval, reranking, chunking, memory strategies
o Vector DBs (faiss, aisearch, opensearch, PG vector etc).
Design prompt-engineering strategies:
o Instruction-following
o ReAct (Reasoning + Acting)
o Chain-of-thought structuring
o 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:
o Tool-calling agents
o Planner-executor systems
o Multi-agent communication systems
o Hierarchical agent architectures
o Deep Agents
Integrate memory systems:
o episodic memory
o semantic memory
o 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:
o APIs
o Code execution environments
o Knowledge bases
o 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 systemlevel transparency.
6. Cloud ML-Ops & Quality
ML Modelling, data drift, concept drift, model quality monitoring.
Handson 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)
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Required Skills & Experience
5-10 years total experience, with 2-4+ years hands-on GenAI.
Hands-on expertise with:
o LangChain, LangGraph
o LangSmith (tracing, metrics, evaluations)
o MCP tooling and agent tool integration
o ReAct, Tree of Thoughts, multi-agent orchestration
o RAG patterns and vector databases
Strong coding expertise in Python.
Classical ML foundations (tree models, regression, etc.).
Experience working with LLM APIs and/or open-source LLMs.
Experience building and debugging production-quality GenAI pipelines.
Aws/azure
GIT Ops
Prior experience building complex multi-agent systems for real-world applications.
Knowledge of multi-modal LLMs (vision, speech, code).
Familiarity with structured evaluation of LLM systems (hallucination tests, safety assessments etc ).
Experience in enterprise-grade LLM deployments.
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