AI Software Engineer - NLP​/LLM

Overview

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.

• 5+ years of demonstrated experience building production-grade machine learning systems with measurable impacts; expertise in NLP and search and recommendation systems is preferred.
• Hands-on experience with large language model (LLM) applications and AI agents, including retrieval-augmented generation, prompt optimization, fine-tuning, agent design, and evaluation methodologies; familiarity with prompt optimization frameworks like DSPy is preferred.
• Deep expertise in machine learning models and systems design, including classic models (e.g., XGBoost), modern deep learning and graph machine learning architectures (e.g., transformers-based models, graph neural networks (GNN)), and reinforcement learning systems.
• Proven ability to take models and agents from research to production, including optimization for latency and cost, implementation of monitoring and tracing, and development of reusable platforms or frameworks.
• Strong technical leadership and mentorship skills, with a track record of growing engineers, improving team velocity through automation, documentation, and tooling, and influencing architectural decisions without direct authority.
• Excellent communication and strategic thinking abilities, capable of aligning technical decisions with business outcomes, navigating ambiguity, and driving cross-functional collaboration.

Bachelor’s degree or higher in Computer Science, Engineering, or a related field.

As an Associate Director of Data Science, you will be driving unscoped, high-impact projects that apply cutting-edge AI and machine learning techniques in production. You’ll collaborate closely with cross-functional partners, including product management, software engineers, and business stakeholders, to identify opportunities, scope and deliver solutions, and measure their impact. This role blends technical leadership and hands-on execution: you’ll guide project direction, ensure quality, contribute code, and introduce best practices that scale team effectiveness.

Your ability to influence strategy and translate complex ideas into business value will be key to advancing the organization’s data science and AI/ML agenda.

• Design and deploy end to end AI and machine learning solutions including machine learning and graph-based models, natural language processing (NLP) models, and large language model (LLM) based AI agents. Build robust pipelines for data ingestion, feature engineering, model training, validation, and real-time or batch inference.
• Develop and integrate large language model (LLM) applications using techniques such as fine-tuning, retrieval-augmented generation, and reinforcement learning. Build autonomous agents capable of multi-step reasoning and tool use in production environments.
• Lead the full model and agent development lifecycle, from problem definition and data exploration through experimentation, implementation, deployment, and monitoring. Ensure solutions are scalable, reliable, and aligned with business goals.
• Advocate and implement machine learning operations (MLOps) best practices including data monitoring and…