AI Research Scientist- Associate Director

Responsibilities

• Lead applied research initiatives related to AI models, advanced AI agent patterns, optimizing knowledge retrieval, among other areas, to solve complex audit challenges.
• Collaborate closely with AI engineers and product managers to translate cutting-edge research in areas like context engineering and RAG into scalable, production-ready components.
• Design and champion a rigorous experimentation framework to measure, validate, and ensure the quality and reliability of AI-driven outcomes.
• Drive the team's research agenda by identifying emerging trends in generative AI, proposing innovative solutions, and mentoring junior scientists and engineers to foster a culture of continuous learning and excellence.
• Design and implement processes to perform for AI model training; from infrastructure, to data labelling, evaluations, among other areas; work to ensure models can be deployed and managed in production.
• Architect and prototype novel research and new solutions; share findings with colleagues across KPMG globally to advance wider AI initiatives.

Qualifications

• Minimum eight years of recent experience in applied AI/ML research, with a focus on natural language processing, information retrieval, and large-scale language models.
• Bachelor's degree from an accredited college/university in Computer Science, AI, Statistics, or a related quantitative field is required; advanced degree (Ph.D. or Master's) from an accredited college or university.
• Deep theoretical knowledge and practical expertise in key research areas such as knowledge retrieval (RAG), AI agent architecture, and techniques for fine-tuning and evaluating language models.
• Prominent experience designing and implementing the end-to-end lifecycle for AI models, including data processing, labeling, training, and production deployment (MLOps).
• Extensive hands-on experience prototyping and experimenting with modern AI frameworks like LangChain/LangGraph or Semantic Kernel.
• Exceptional ability to translate ambiguous business challenges into defined research problems and articulate complex findings and their strategic implications to both technical and executive audiences.