Moody's Corporation
Principal Software Engineer-AI

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Skills And Competencies
- 12+ years of software engineering experience, including 5+ years leading engineering teams and delivering complex, large-scale, multi-team initiatives with measurable business impact
- Experience leading multiple teams or groups of teams, including managing engineering managers, technical leads, and staff-level engineers with accountability for delivery outcomes across a portfolio
- Strong technical depth in TypeScript, Python, C#, or similar technologies, with the ability to engage credibly in architecture and code reviews, challenge design decisions, and assess technical risk
- Deep expertise in enterprise AI applications built on large language models, including agents, retrieval augmented generation, orchestration frameworks, model optimization, and evaluation frameworks
- Hands-on experience building AI/ML applications (RAG, agent frameworks, MCPs, skills, harnesses) and taking them to production or in platforming (LLM or MCP gateway, agentic runtime, auth, data retrieval, eval tooling)
- Experience running AI systems in production at scale, including observability, cost and capacity planning, regression detection, and incident response for AI-powered applications
- Experience operating production distributed systems on AWS/Azure, with a strong grasp of reliability, observability, and incident response at scale
- Deep knowledge of cloud-native technologies, serverless applications, event-driven architectures, data and inference pipelines, relational, NoSQL, and vector databases, and modern software architecture patterns
- Proven track record of owning multi-year technical strategy and architectural roadmaps, guiding teams from AI prototype through production deployment, and influencing organizational change
- Deep expertise in artificial intelligence, with a track record of implementing advanced AI solutions to drive strategic transformation and operational efficiency. Strong experience using AI tools to lead innovation initiatives. Demonstrated leadership in managing AI-related risks, ensuring ethical governance, and fostering a culture of responsible AI adoption across the organization
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Education
- Bachelor's or master's degree in Computer Science, Engineering, Artificial Intelligence, or a related discipline, or equivalent professional experience
Responsibilities
- Lead the strategy, delivery, and evolution of mission-critical AI platforms and intelligent applications while driving engineering excellence across the Digital Content and Innovation organization.
- Define and execute long-term technical strategies while owning the multi-year architecture and technology roadmap for AI platforms and applications
- Lead, grow, and organize multiple engineering teams, including engineering managers, technical leads, and staff-level engineers, with accountability for delivery outcomes across the portfolio
- Drive architectural decisions, organizational alignment, and integration of AI-powered solutions using large language models, retrieval augmented generation, orchestration frameworks, and agentic workflows
- Establish engineering excellence through architecture reviews, code reviews, automated testing, observability, security standards, operational excellence, and machine learning operations practices
- Own budget planning and cost management, including headcount, cloud infrastructure, model consumption, tooling investments, vendor relationships, and build-versus-buy decisions
- Partner with product, business, risk, legal, compliance, and audit stakeholders to maximize customer value and implement responsible AI governance practices
- Attract, develop, and retain engineering talent through mentoring, succession planning, performance coaching, and the cultivation of future engineering leaders
- Define and report on key metrics that demonstrate platform adoption, delivery performance, reliability, cost efficiency, and measurable business impact


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About the Team
Our Digital Content and Innovation team is responsible for building and deploying intelligent, AI-powered solutions that drive innovation across Moody's products and platforms. The team accelerates the development of next-generation analytical capabilities, enables smarter client experiences, and advances Moody's leadership in applied artificial intelligence. As Principal Engineer, you will define the technical vision, guide strategic investments, build high-performing teams, and help transform applied AI into scalable, production-ready capabilities that deliver measurable value responsibly and at scale.
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