KI
Senior - AIML - DEQ
Accepting applicationsKPMG India · Bengaluru, Karnataka, India
Full-Time Mid_senior AIPythonaiaterf
Posted
1 Jun
Category
Test
Experience
Mid_senior
Country
India
Job Description
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.
________________________________________
Key Responsibilities
GenAI / LLM Application Development
Build GenAI applications using:
oLangChain, LangGraph
Implement RAG architectures with:
oRetrieval, reranking, chunking, memory strategies
oVector DBs (faiss, aisearch, opensearch, PG vector etc).
Design prompt-engineering strategies:
oInstruction-following
oReAct (Reasoning + Acting)
oChain-of-thought structuring
oSelf-reflection and planning loops
Evaluation Strategy
oImplement evaluation frameworks for Classical ML and GenAI systems, covering statistical validation, reliability, and robustness.
oAssess LLM outputs, RAG pipelines, and agent workflows for grounding quality, relevance, and retrieval accuracy (e.g., recall@k, precision@k).
oUse LangSmith for tracing, automated evaluations, regression testing, and continuous system level quality monitoring
Agentic System Architecture
Build agentic workflows:
oTool-calling agents
oPlanner–executor systems
oMulti-agent communication systems
oHierarchical agent architectures
oDeep Agents
Integrate memory systems:
oepisodic memory
osemantic memory
ovector-based long-term knowledge
Implement evaluation frameworks for agentic systems using LangSmith.
Model Context Protocol (MCP) & Tooling
Implement MCP servers for external tool connectivity.
Build tools that allow agents to interact with:
oAPIs
oCode execution environments
oKnowledge bases
oCompany applications
Classical ML (Foundational DS Skills)
Apply ML models to structured/unstructured data.
Conduct feature engineering, model selection, hyperparameter tuning.
Build interpretable models where required.
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.
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 & Experience
5–10 years total experience, with 2–4+ years hands-on GenAI.
Hands-on expertise with:
oLangChain, LangGraph
oLangSmith (tracing, metrics, evaluations)
oMCP tooling and agent tool integration
oReAct, Tree of Thoughts, multi-agent orchestration
oRAG 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.
Qualifications
BTECH
Show more Show less
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.
________________________________________
Key Responsibilities
GenAI / LLM Application Development
Build GenAI applications using:
oLangChain, LangGraph
Implement RAG architectures with:
oRetrieval, reranking, chunking, memory strategies
oVector DBs (faiss, aisearch, opensearch, PG vector etc).
Design prompt-engineering strategies:
oInstruction-following
oReAct (Reasoning + Acting)
oChain-of-thought structuring
oSelf-reflection and planning loops
Evaluation Strategy
oImplement evaluation frameworks for Classical ML and GenAI systems, covering statistical validation, reliability, and robustness.
oAssess LLM outputs, RAG pipelines, and agent workflows for grounding quality, relevance, and retrieval accuracy (e.g., recall@k, precision@k).
oUse LangSmith for tracing, automated evaluations, regression testing, and continuous system level quality monitoring
Agentic System Architecture
Build agentic workflows:
oTool-calling agents
oPlanner–executor systems
oMulti-agent communication systems
oHierarchical agent architectures
oDeep Agents
Integrate memory systems:
oepisodic memory
osemantic memory
ovector-based long-term knowledge
Implement evaluation frameworks for agentic systems using LangSmith.
Model Context Protocol (MCP) & Tooling
Implement MCP servers for external tool connectivity.
Build tools that allow agents to interact with:
oAPIs
oCode execution environments
oKnowledge bases
oCompany applications
Classical ML (Foundational DS Skills)
Apply ML models to structured/unstructured data.
Conduct feature engineering, model selection, hyperparameter tuning.
Build interpretable models where required.
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.
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 & Experience
5–10 years total experience, with 2–4+ years hands-on GenAI.
Hands-on expertise with:
oLangChain, LangGraph
oLangSmith (tracing, metrics, evaluations)
oMCP tooling and agent tool integration
oReAct, Tree of Thoughts, multi-agent orchestration
oRAG 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.
Qualifications
BTECH
Show more Show less