DEQ - Senior - AIML

Role overview

DEQ - Senior - AIML at KPMG

Required qualifications

• 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.
• Equal employment opportunity information KPMG India has a policy of providing equal opportunity for all applicants and employees regardless of their color, caste, religion, age, sex/gender, national origin, citizenship, sexual orientation, gender identity or expression, disability or other legally protected status.
• KPMG India values diversity and we request you to submit the details below to support us in our endeavor for diversity.
• Providing the below information is voluntary and refusal to submit such information will not be prejudicial to you.
• BTech

Employment details

• Location: Bangalore [Karnataka] [India]

About the employer

• 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. ________________________________________ 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 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) ________________________________________ Required Skills &