KPMG India is currently hiring for the position of Associate Consultant / Consultant – AI Engineering & Generative AI Solutions, based in Mumbai, Maharashtra, India.
This is a compelling opportunity for professionals who have hands-on expertise in Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), Machine Learning, and Python development, and who are looking to work on enterprise-scale AI solutions within one of the world’s leading professional services firms.
The role sits at the intersection of cutting-edge AI research and real-world enterprise deployment, offering candidates the chance to build production-grade systems that directly influence business outcomes in Risk, Treasury, and broader transformation initiatives.
With over 100 applicants already showing interest and the position being actively reviewed by the hiring team, this is a role that is generating significant attention among AI engineering professionals.
Job Details
- Company: KPMG India
- Job Title: Associate Consultant / Consultant – AI Engineering & Generative AI Solutions
- Location: Mumbai, Maharashtra, India
- Work Mode: On-site
- Employment Type: Full-time
- Experience Required: 1 to 6 Years
- Grade/Level: Associate Consultant / Consultant
- Posting Status: Posted 3 days ago, over 100 applicants, actively reviewing applications
- Applicant Insights: 39% of applicants hold a Bachelor’s Degree; 63% of applicants are at entry level
About the Role
KPMG India is looking for highly skilled AI Engineers and Consultants with strong expertise across Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), Machine Learning, and Python development.
This is not a purely research-oriented position; rather, the ideal candidate will play a central role in designing, building, and deploying enterprise-scale AI solutions with a specific focus on Risk, Treasury, and business transformation initiatives across the organization’s client base.
Key Responsibilities
AI Solution Development
- Design, develop, and deploy end-to-end AI applications by integrating large language models (LLMs), APIs, enterprise data sources, and user-facing interfaces.
- Build scalable, production-ready solutions that leverage Generative AI, Agentic AI, RAG, GraphRAG, and various foundation models.
- Develop AI-powered applications addressing forecasting needs, information retrieval, document intelligence, and process automation across business functions.
- Implement robust evaluation frameworks that measure model performance, response quality, output accuracy, and overall business impact, ensuring that deployed solutions actually deliver measurable value.
Generative AI & Agentic Workflows
- Design and implement intelligent, agent-based workflows using frameworks such as LangChain and LangGraph, enabling AI systems to carry out multi-step tasks autonomously.
- Develop Retrieval-Augmented Generation (RAG) and GraphRAG solutions tailored for enterprise knowledge management and decision support, allowing organizations to surface accurate, context-aware information from large internal knowledge bases.
- Create and refine prompt engineering strategies to improve the reliability, performance, and overall user experience of AI-driven solutions.
- Optimize AI agents to handle complex reasoning tasks, coordinate workflow orchestration across multiple systems, and execute tasks with minimal human intervention.
Model Engineering & Optimization
- Customize and optimize open-source LLMs, OCR engines, and document intelligence models so that they meet the specific needs of enterprise deployment scenarios.
- Adapt AI models that were originally built for GPU-rich environments so that they run effectively on CPU-constrained, secure, on-premises infrastructure — a critical requirement for many risk-sensitive clients.
- Apply a range of optimization techniques, including:
- Quantization
- Model compression
- Memory optimization
- Batching
- Caching
- Performance tuning
- Continuously evaluate emerging AI architectures, new foundation models, and open-source projects to identify opportunities for improving existing solutions or building new ones.
Data Engineering & Integration
- Build and maintain scalable data ingestion and ETL (Extract, Transform, Load) pipelines that feed clean, reliable data into AI systems.
- Integrate both structured and unstructured data from a wide range of internal and external sources, using APIs, web scraping techniques, and automation frameworks.
- Use tools such as BeautifulSoup (BS4), Selenium, and REST APIs to acquire and enrich data as needed for various AI use cases.
- Maintain a strong focus on data quality and governance, ensuring that AI applications are built on trustworthy, well-processed data foundations.
Research & Innovation
- Analyze research papers, technical publications, and open-source repositories on an ongoing basis to stay ahead of emerging AI capabilities and industry trends.
- Prototype and evaluate new LLMs, OCR technologies, document intelligence platforms, and foundation models before they are considered for production use.
- Translate research findings into practical, innovative recommendations that address real business and technical challenges faced by clients and internal teams alike.
Documentation & Governance
- Create and maintain comprehensive technical documentation, including architecture diagrams, deployment guides, and operational runbooks, to ensure solutions are maintainable and well understood across teams.
- Actively support solution reviews, code quality assessments, and production readiness activities to ensure that deployed systems meet KPMG’s internal quality bar.
- Ensure that all AI solutions comply with enterprise security policies, governance frameworks, and established deployment standards, which is especially important given the sensitive nature of Risk and Treasury-related work.
Mandatory Requirements
Programming & AI Development
- Strong, hands-on programming experience in Python is essential.
- Practical experience with libraries and frameworks such as Pandas, Polars, PyTorch, LangChain, LangGraph, FastAPI, and Streamlit.
- Demonstrated ability to build modular, scalable, maintainable, and production-grade AI applications rather than one-off prototypes.
Generative AI & Foundation Models
- Strong, applied experience with Retrieval-Augmented Generation (RAG), GraphRAG, Agentic AI frameworks, and vector databases combined with semantic search.
- Familiarity working with tabular foundation models such as TabPFN (or similar), and time-series foundation models such as TimesFM (or similar).
Model Optimization
- Hands-on experience reviewing, modifying, and deploying open-source LLM and OCR codebases.
- Solid understanding of quantization, model compression, memory optimization, inference acceleration, and strategies for deploying models in resource-constrained environments.
- Demonstrated experience deploying AI models within secure, on-premises enterprise environments — a key differentiator for this role given the client base KPMG serves.
Preferred Skills
- Experience with prompt engineering and formal LLM evaluation techniques.
- Prior work with OCR and document intelligence solutions.
- Knowledge of AI application monitoring and model observability practices.
- Familiarity with vector databases such as FAISS, ChromaDB, Pinecone, or Milvus.
- Comfort working with Docker, Kubernetes, CI/CD pipelines, and major cloud platforms.
- Prior experience working within Risk, Treasury, Banking, or broader Financial Services domains is considered a strong plus.
- The ability to interpret cutting-edge AI research and translate it into practical, deployable business solutions.
About the Company
KPMG India entities are established under the laws of India and are owned and managed by established Indian professionals. Founded in September 1993, KPMG’s entities in India have rapidly built a significant and competitive presence in the country over the past three decades.
Today, the organization operates from offices across 14 cities, including Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara, and Vijayawada.
How to Apply
Interested candidates can apply directly through LinkedIn by clicking the link below. The posting is currently listed as an active job with the hiring team reviewing applications on an ongoing basis, so early application is recommended given the volume of interest already received.