AI Engineer

About us

KPMG Delivery Network India (KDNI) is a diverse entity spread across multiple cities in India. We are an important part of the KPMG Delivery Network (KDN), a global organization that supports KPMG member firms in delivering global priority solutions to their clients across a number of different industries. Our team of over 3,000 professionals in India are known for their technical acumen and business insights, that come together to deliver exceptional client service.

When you join KDN, you’ll be a part of the wider KPMG global network working alongside some of our profession’s most skilled practitioners on rewarding programs and initiatives that are changing the way business operates, delivering value to the clients of KPMG member firms and driving positive change in the communities we serve.

As a KDN India colleague you will be helping to accelerate new ways of working, using cutting-edge technology to create increased value, and working together with member firm colleagues across the globe to drive sustainable growth and help achieve our ambition to be the most trusted and trustworthy professional services firm.

Job Discription and Skills

Experience - 18yrs - 25 yrs

Location - Bangalore

Work Mode - Hybrid

Notice Period - Immeditate to 30 days only

Skill - AI Engineer with Strong experience in coding

AI leader will shape the engineering vision, drive cross-functional squads, and ensure that all AI solutions adhere to KPMG’s Trusted AI Governance, Zero-Trust frameworks, and multi-cloud operational standards.

Responsibilities :

1. AI Engineering & Architecture Leadership

• Lead full lifecycle AI engineering from prototype to MVP to production across KPMG Audit, Tax, and Advisory use cases.
• Architect scalable Agentic AI systems integrated with Foundry pipelines, Agent Factory orchestration, and KDO lakehouse connectivity.
• Define modular AI agent architecture (stateless / headless / memory-enabled) using LangChain, CrewAI, LlamaIndex, and Azure AI Studio.
• Drive model optimization, fine-tuning, and RLHF strategies for both proprietary and open-weight models (GPT, Claude, Llama, Falcon).

2. Platform Ownership – AI Foundry, Agent Factory

3. Data & Integration Leadership

4. Delivery & Governance

5. Client-Facing & Internal Transformation

Required Technical Competencies

Leadership & Behavioral Competencies

Educational & Professional Background