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.
Skills and Competencies
- 8+ years of experience in software engineering, with deep hands-on experience designing, coding, testing, and operating scalable, resilient, production-grade backend systems and cloud-native services
- Expert-level coding capability in modern programming languages such as Python, TypeScript, or similar, with the ability to personally contribute high-quality production code while guiding technical direction
- Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques
- Proven ability to take complex AI solutions from prototype to production, making practical engineering trade-offs across performance, scalability, reliability, security, maintainability, and cost
- Deep experience with system integrations and modern interoperability protocols, including Model Context Protocol (MCP) and agent-to-agent (A2A) standards, with the ability to define integration patterns and shape how these are adopted at scale
- Hands-on breadth across major cloud platforms and marketplace ecosystems, including Microsoft Azure, Amazon Web Services, Google Cloud Platform, and Databricks, with experience deploying and operating integrations in production across these environments
- Strong experience designing and implementing application programming interfaces, distributed systems, event-driven architectures, and data pipelines, including batch ingestion, streaming, and connector frameworks for enterprise data delivery
- Proven experience delivering technical proofs of concept, live demonstrations, and prototype solutions to partners and enterprise clients, and turning the successful ones into durable, production-grade systems
- Strong understanding of enterprise OAuth flows and secure authentication patterns for multi-platform integrations
- Demonstrated ability to influence technical direction while remaining close to the codebase, mentoring engineers through design reviews, code reviews, pairing, debugging, and hands-on problem solving
- Demonstrated commitment to responsible AI practices, including AI risk awareness, ethical use, governance, evaluation, monitoring, and continuous improvement of AI-enabled products and services
- Excellent collaboration and communication skills, with the ability to lead solution architecture design, technical diagramming, and whiteboarding sessions for both engineering and executive audiences, and to represent the organisation credibly in external technical engagements
Education
- Bachelor's degree or higher in Computer Science, Software Engineering, Artificial Intelligence, or a related technical field, or equivalent practical experience
Responsibilities
Design, code, and lead delivery of scalable AI platforms and intelligent applications that bring emerging AI capabilities into production across partner ecosystems
- Act as a hands-on technical leader, spending significant time designing, coding, reviewing, debugging, and improving production systems that support AI-powered products and partner integrations
- Own the end-to-end technical architecture for the AI partner ecosystem, producing system diagrams, architecture documentation, and design standards that align engineering efforts with product vision and stakeholder expectations across multiple initiatives
- Architect and evolve the agentic workflows, skills, and tool-use integrations that allow AI systems to interact with Moody's platforms and third-party services via MCP and related protocols, and define the patterns other engineers build against
- Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration
- Build and deliver compelling proofs of concept, live demonstrations, and prototype solutions that showcase platform capabilities and accelerate partner adoption, then harden them into scalable, maintainable production systems
- Serve as the senior technical advisor in client- and partner-facing engagements, effectively communicating complex AI architectures, solution options, and recommendations to both technical and non-technical audiences
- Deploy, monitor, and optimise integrations across cloud environments including Microsoft, AWS, GCP, and Databricks, establishing operational and reliability standards for the team
- Define and champion integration patterns and protocols, including Model Context Protocol (MCP) and A2A, ensuring seamless interoperability across platforms and influencing how these standards are adopted across the organisation
- Track and assess AI partner technical roadmaps from hyperscalers and leading AI providers, identifying opportunities to enhance products and translating partner capabilities into actionable engineering work
- Establish engineering best practices through hands-on contribution, code reviews, technical design reviews, automated testing, observability, and operational excellence
- Champion responsible AI practices including evaluation frameworks, model lifecycle management, prompt versioning, monitoring, and continuous improvement of AI-enabled products
- Partner with product managers, engineering leaders, and business stakeholders to translate strategic priorities into robust, buildable technical solutions, and contribute to build/buy/partner decisions
- Mentor engineers through practical technical coaching, pairing, code reviews, design feedback, documentation, and example-setting as a senior individual contributor
About the team
Our team is responsible for building and deploying intelligent, AI-powered solutions that drive innovation across products and platforms. We shape the product ecosystem through AI, data, and strategic partnerships—embedding large language models, agentic platforms, and advanced analytics into how insight and value are delivered. We partner with leading AI and technology providers including Anthropic, Microsoft, AWS, and Databricks to accelerate innovation and expand platform capabilities
Collaboration is central to how the team works. Engineers contribute across the full AI lifecycle, from experimentation and prototyping through to large-scale production deployment, while helping shape reusable platforms, integration standards, and responsible AI practices
By joining the team, you will work on some of the most exciting challenges in applied AI, contribute directly to production code and architecture, and be part of a culture that values curiosity, innovation, knowledge sharing, and continuous growth
Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.
Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.