Assistant Director - Applied AI Engineer

At Moody's, we unite the brightest minds to turn today's risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it.

We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.

If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity.

• Strong problem‑solving capability, with the ability to break down ambiguous business problems, form and test hypotheses, reason from first principles, and determine when AI is the right solution versus simpler alternatives.
• Confident stakeholder communication and presentation skills, with the ability to explain complex technical concepts to non-technical audiences, present trade‑offs to senior leaders, and translate effectively between engineering, product, and business teams.
• Proven ability in requirements gathering and user experience design, including eliciting and documenting business needs, designing intuitive AI‑powered experiences, and incorporating iterative user feedback.
• Adaptability and comfort working through ambiguity, adjusting technical and delivery approaches as new evidence, feedback, or business priorities emerge.
• Hands‑on applied AI engineering experience, including proficiency in Python, JavaScript, or similar languages, and practical use of modern GenAI and agent frameworks such as Lang Chain, Llama Index, or Smol Agents.
• Experience building, deploying, and maintaining production‑grade AI systems that deliver measurable business value.
• Working knowledge of data management, including data architecture, governance, and privacy considerations, with experience using tools such as Pandas, Num Py, SQL, and a major cloud platform (AWS, Azure, or GCP).
• Practical experience with production reliability, including monitoring, release gates, rollback strategies, incident response, and managing trade‑offs between quality, latency, and cost.
• Experience evaluating emerging AI technologies and frameworks, designing structured bake‑offs, and making evidence‑based recommendations.
• Demonstrates deep expertise in applying AI responsibly, including consideration of ethical use, risk management, and governance in production environments.

• Bachelor’s degree required in Computer Science, Data Science, Statistics, Mathematics, Business, Information Systems, or a related field.
• Master’s degree or PhD welcomed, or equivalent demonstrable experience in applied AI.
• Demonstrated commitment to continuous professional development in AI or machine learning through certifications, publications, conference participation, or similar evidence of staying current in the field.

Operate as a senior individual contributor who partners closely with the business to design, deliver, and embed applied AI solutions from initial concept through to reliable production use.

• Partner with business stakeholders and workstream leads to understand operational challenges and translate them into AI solutions aligned with business priorities.
• Present progress, options, risks, and trade‑offs clearly and confidently to senior leaders, keeping stakeholders and delivery teams aligned.
• Own the end‑to‑end user journey for solutions, including requirements gathering, experience design, rollout planning, user training, and feedback capture.
• Frame and structure problems rigorously before designing solutions, ensuring clarity of scope, assumptions, and success criteria.
• Design, build, and deploy…