AI Developer

Our Innovation and Developer Team
As the backbone of our corporate innovation, our team is dedicated to fostering growth, operational excellence, and a culture of innovation. Our mission is to drive the company forward by designing and developing cutting-edge solutions and internal applications.
Join Us as an AI Developer - Internal AI Platform
KPMG is making a bold, multi-billion dollar investment in artificial intelligence, and at the heart of this transformation is KPMG Workbench - a scalable, multi-agent AI platform built on Microsoft Azure AI Foundry. Workbench is already powering client delivery platforms like KPMG Clara, Velocity, and Digital Gateway, and features over 50 AI agents with nearly 1,000 more in development. This is not just a technological leap - it's a global commitment to AI excellence and innovation [1][2][3].
As part of KPMG Vietnam's internal AI team, you'll help build and scale AI solutions that are used across the firm, contributing directly to our transformation journey.
Why You'll Love Working Here:
Global Collaboration: Work closely with the KPMG AI Global Platform, gaining support from experts across KPMG member firms worldwide.
Impactful Innovation: Contribute to building AI solutions that are deployed across the firm, enhancing productivity and decision-making.
Advanced AI Applications: Apply cutting-edge AI models to analyze and predict insights from KPMG's rich internal data sources.
What You'll Learn and Lead:
You'll gain hands-on experience with the end-to-end AI transformation program, from ideation to deployment. This includes understanding the full lifecycle of AI adoption within a professional services firm - a critical capability that leaders across industries are prioritizing as AI becomes central to business strategy.
Key Responsibilities:
1. Design, develop, and deploy AI models using machine learning (ML), deep learning, and natural language processing (NLP) techniques based on company platforms.
2. Collaborate with cross-functional teams to understand business requirements and translate them into AI solutions.
3. Collect, preprocess, and analyze datasets to train and evaluate models, optimize and fine-tune algorithms for performance and scalability
4. Integrate AI models into existing applications, monitor and maintain AI systems in production to ensure reliability and accuracy.
5. Document system architecture, workflows and technical decisions.
6. Work flexibly across multiple projects and deadlines.