Location
London

Hours
Full Time

About the Role
At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities by fostering an inclusive environment where everyone feels welcome to be themselves, exchange ideas, think innovatively, and listen meaningfully to each other and our customers. Moody’s is transforming how the world sees risk as a global leader in ratings and integrated risk assessment, advancing AI to move from insight to action—enabling intelligence that understands complexity and responds to it.

The Modeling & Quant Analytics - AI & Agentic Model Validation role involves independent evaluation and challenge of models, scorecards, and agents used in credit rating activities across asset classes, with a focus on AI model risk management. You will bring strong, practical expertise in Generative and agentic AI and actively advance Moody’s approach to evaluating and governing these systems.

Key responsibilities include carrying out independent evaluation and challenge of AI-enabled tools, agents, and quantitative models, performing AI model risk management activities, designing and delivering complex validation analyses, and maintaining clear separation between model development and independent review. You will also build AI model risk capability within the team, share expertise, and promote rigorous execution standards and continuous improvement.

The role is part of the MRG Quantitative Review team, which independently reviews and validates quantitative models and scorecards supporting credit ratings to ensure conceptual soundness, appropriate calibration, and governance compliance.

Experience
- Strong understanding of AI model risk management, including risks and controls specific to Generative AI and agentic AI, and their implications for model validation and governance
- Practitioner-level agentic AI skills with demonstrated experience building, deploying, and evaluating AI agents and multi-agent systems using modern agent frameworks
- Practical knowledge of agent evaluation methods including task-success and trajectory testing, hallucination and drift detection, robustness testing, benchmarking, bias and fairness assessment, alignment analysis, adversarial testing, and red-teaming
- Strong LLM/GenAI engineering fluency including prompting strategies, retrieval-augmented generation, context and memory design, function/tool calling, guardrails, observability, and failure-mode analysis
- Proven track record of advancing AI systems from prototype to production with attention to reliability, safety, and responsible AI controls
- Proficiency in programming languages such as R, Python, MATLAB, and SQL; knowledge of C++ preferred
- Experience in model validation, model risk management, or independent review is advantageous
- Relevant professional or research experience in AI, machine learning, quantitative analytics, software engineering, or model risk management

About you
- Drive and ownership: energetic, takes initiative, builds trusted relationships, role models growth mindset and effective challenge
- Highly organized, efficient, detail-oriented, able to prioritize competing demands and meet tight deadlines
- Strong communicator able to articulate complex ideas clearly to both technical and non-technical audiences

Qualifications
- Strong academic background in a technical or quantitative field such as computer science, artificial intelligence, machine learning, mathematics, physics, or engineering
- Preference for candidates holding a post-graduate degree
- Demonstrated, applied agentic AI capability is highly valued alongside formal credentials