KPMG Recruitment 2026 – Associate Consultant / Consultant | AI Engineering | Mumbai

KPMG Recruitment 2026: KPMG in India is hiring for the position of Associate Consultant / Consultant – AI Engineering & Generative AI Solutions in Mumbai. The opportunity is aimed at professionals with experience in Python, Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), Machine Learning, and production-oriented AI application development.

The role involves designing, developing, and deploying enterprise AI solutions for business transformation initiatives. Candidates should be comfortable working with modern AI frameworks, foundation models, enterprise data, and production-grade application architectures.

KPMG Recruitment 2026 – Job Details

Company	KPMG
Job Role	Associate Consultant / Consultant
Specialization	AI Engineering & Generative AI Solutions
Qualification	B.E / B.Tech / M.E / M.Tech
Experience	1–6 Years
Location	Mumbai, Maharashtra
Employment Type	Full Time
Job Category	IT / AI Engineering
Salary / CTC	Best in Industry
Last Date	ASAP

About the KPMG AI Engineering Role

This position is focused on building enterprise-scale AI solutions using modern Generative AI and Machine Learning technologies. Engineers and consultants in this role will contribute to AI applications that support areas such as risk, treasury, and broader business transformation.

The position goes beyond experimenting with AI models. Candidates are expected to understand how to build modular, scalable, maintainable, secure, and production-ready applications and integrate AI capabilities into real business workflows.

Eligibility Criteria

Educational Qualification

• B.E or B.Tech in a relevant technical discipline.
• M.E or M.Tech candidates can also apply.

Experience

• 1–6 years of relevant professional experience.
• Hands-on experience in Python-based AI or Machine Learning development is expected.
• Experience building practical Generative AI solutions is highly relevant.
• Production-oriented application development experience is valuable.

Core Technical Skills

• Python
• Pandas
• Polars
• PyTorch
• LangChain
• LangGraph
• FastAPI
• Streamlit
• Retrieval-Augmented Generation (RAG)
• GraphRAG
• Agentic AI frameworks
• Vector databases
• Semantic search
• Large Language Models
• Machine Learning

Generative AI Requirements

Candidates should have practical knowledge of modern Generative AI architectures rather than only theoretical familiarity. The role specifically highlights RAG, GraphRAG, agentic AI frameworks, vector databases, and semantic search.

Retrieval-Augmented Generation

Understand how retrieval systems can provide relevant enterprise information to language models before generation. Candidates should be familiar with document ingestion, chunking, embeddings, retrieval, ranking, and context construction.

GraphRAG

Graph-based retrieval approaches can connect entities and relationships within enterprise information. Candidates should understand the situations where graph-based retrieval can complement conventional vector search.

Agentic AI

Agentic systems can use tools, reasoning workflows, memory, and multiple steps to complete complex tasks. Candidates should understand how to design reliable agent workflows and control their interaction with external tools and data.

Vector Databases and Semantic Search

Knowledge of embeddings, vector indexing, similarity search, metadata filtering, and retrieval pipelines is important for developing production RAG applications.

Python and AI Application Development

Strong hands-on Python experience is one of the central requirements of the position. Candidates should be able to create clean and maintainable Python applications and work with modern AI and data-processing libraries.

Experience with frameworks such as FastAPI and Streamlit can help in building APIs, internal tools, demonstrations, and AI-powered applications.

Machine Learning and Foundation Models

The role involves working with modern Machine Learning and foundation-model technologies. Candidates should understand how AI models are integrated into applications and how model selection affects performance, cost, latency, and reliability.

The requirements also mention experience with tabular and time-series foundation models such as TabPFN and TimesFM, or comparable technologies.

Model Optimization

AI applications often need to run efficiently within practical infrastructure limits. The role therefore includes experience with reviewing, modifying, and deploying open-source LLM and OCR codebases.

Relevant optimization concepts include:

• Quantization
• Model compression
• Memory optimization
• Inference acceleration
• Resource-constrained deployment

Key Responsibilities

• Design and develop enterprise AI solutions for business transformation initiatives.
• Build production-grade Generative AI applications.
• Develop AI workflows using Python and modern AI frameworks.
• Implement RAG and GraphRAG-based solutions.
• Develop agentic AI workflows and tool-integrated applications.
• Work with vector databases and semantic-search technologies.
• Integrate foundation models into enterprise applications.
• Review and modify open-source LLM and OCR implementations.
• Optimize models for practical deployment environments.
• Support secure and scalable AI application architecture.
• Contribute to solution reviews and code-quality assessments.
• Help ensure production readiness of AI solutions.
• Follow enterprise security, governance, and deployment standards.
• Collaborate with technical and business stakeholders.

Production-Ready AI Development

Building a proof-of-concept AI application is different from delivering a production system. Candidates should understand concerns such as reliability, observability, security, scalability, latency, data quality, model behavior, and deployment processes.

When preparing for this position, focus on projects where you have taken an AI idea beyond a simple notebook and turned it into a usable application or service.

Enterprise AI and Business Transformation

The role connects AI engineering with business transformation. Candidates should therefore be able to understand a business problem, determine where AI can add value, select an appropriate technical approach, and communicate limitations and expected outcomes.

AI solutions for enterprise environments also need to account for data security, governance, access controls, reliability, and responsible use of AI technologies.

Security and Governance

Enterprise AI systems can process sensitive business information. Candidates should understand the importance of secure data handling, controlled access, appropriate model usage, logging, and governance.

The role expects solutions to meet enterprise security and deployment standards rather than treating security as an afterthought.

Who Can Apply?

This opportunity is suitable for professionals with 1–6 years of experience and a B.E/B.Tech or M.E/M.Tech qualification who have practical experience in AI engineering and Python development.

Candidates with hands-on experience in Generative AI, RAG, Agentic AI, Machine Learning, LLMs, vector databases, and production AI applications are particularly aligned with the role.

How to Prepare for KPMG AI Engineering Recruitment

1. Strengthen Python

Revise Python fundamentals, object-oriented programming, data processing, APIs, asynchronous programming, error handling, testing, and clean-code practices.

2. Build a Complete RAG Application

Create a practical RAG application with document ingestion, chunking, embeddings, vector search, retrieval, prompt construction, response generation, and evaluation.

3. Learn GraphRAG

Understand how entities and relationships can be represented as graphs and how graph retrieval can improve answers involving connected information.

4. Practise Agentic AI

Build an agent that can use tools or APIs to complete a multi-step task. Focus on reliability, tool validation, error handling, and controlling the workflow.

5. Learn Vector Databases

Understand embeddings, similarity search, indexing, metadata filtering, and retrieval evaluation.

6. Learn LangChain and LangGraph

Practise building structured AI workflows using LangChain and graph-based workflows using LangGraph.

7. Build APIs with FastAPI

Learn how to expose an AI model or RAG pipeline through a clean API and handle authentication, validation, errors, and request processing.

8. Understand Model Optimization

Revise quantization, memory optimization, inference speed, model compression, and resource constraints.

9. Learn AI Evaluation

Understand how to evaluate retrieval quality, factuality, relevance, latency, and application reliability instead of judging an AI system only by a few example responses.

10. Prepare an Enterprise-Style Project

Build one polished project that demonstrates architecture, security considerations, data handling, AI integration, testing, deployment, and documentation.

Project Ideas for Candidates

• Enterprise document RAG assistant
• Graph-based knowledge assistant
• Multi-step AI research agent
• Financial or risk-analysis AI assistant
• AI-powered document classification system
• Time-series forecasting application
• Enterprise knowledge-search platform
• AI workflow exposed through a FastAPI service

Application Checklist

• Updated resume
• B.E/B.Tech/M.E/M.Tech qualification
• 1–6 years of relevant experience
• Strong Python programming skills
• Generative AI experience
• RAG / GraphRAG knowledge
• Agentic AI experience
• Vector database and semantic-search knowledge
• Experience with modern AI frameworks
• Production-oriented AI project experience

Frequently Asked Questions

What is the KPMG Recruitment 2026 role?

The position is Associate Consultant / Consultant – AI Engineering & Generative AI Solutions.

Where is the job located?

The role is based in Mumbai, Maharashtra.

What qualification is required?

Candidates should have a B.E/B.Tech or M.E/M.Tech qualification in a relevant field.

How much experience is required?

The listed experience range is 1–6 years.

Can freshers apply?

The listed minimum experience is 1 year, so this particular opening should not be treated as a fresher-only position.

Which programming language is required?

Strong hands-on Python programming experience is a key requirement.

What Generative AI skills are required?

Important areas include RAG, GraphRAG, Agentic AI, vector databases, semantic search, foundation models, and production AI application development.

Is LangChain required?

Experience with LangChain is listed among the technical requirements, along with LangGraph.

Is Machine Learning knowledge required?

Yes. The role is focused on AI engineering and Generative AI solutions and includes Machine Learning and foundation-model technologies.

Does the role involve LLMs?

Yes. Candidates may work with open-source LLM codebases and foundation-model technologies as part of enterprise AI development.

Does the role involve model optimization?

Yes. Relevant areas include quantization, model compression, memory optimization, inference acceleration, and resource-constrained deployments.

What is the salary?

The available job information describes compensation as Best in Industry; a specific numerical CTC is not disclosed.

What is the last date to apply?

The opening is listed with an ASAP deadline. Candidates should verify the current availability on KPMG’s official careers portal before applying.

How to Apply for KPMG Recruitment 2026

Interested candidates should apply through the official KPMG in India careers portal and search for the Associate Consultant / Consultant – AI Engineering & Generative AI Solutions opportunity in Mumbai.

Official Careers: KPMG in India Careers

KPMG recommends using its official search-and-apply process for current openings. The firm also states that it does not charge candidates fees or security deposits during recruitment. :contentReference[oaicite:3]{index=3}