Lead Machine Learning Engineer - AI Innovation Teams

Lead Machine Learning Engineer – AI Innovation Teams
As one of the world’s top three credit ratings agencies, Fitch Ratings plays a critical role in global capital markets by providing credit analysis, ratings, research, and commentary to financial market participants. For over 100 years, Fitch Ratings has been creating value for global markets through its rigorous analysis and deep expertise, which have resulted in a variety of market leading tools, methodologies, indices, research, and analytical products.

Fitch Ratings is part of Fitch Group, a global leader in financial information services with operations in more than 30 countries, which also includes Fitch Solutions. With dual headquarters in London and New York, Fitch Group is owned by Hearst.

Join our fast‑growing Toronto innovation hub, where we’re building production‑grade AI to reshape global credit decisions. Here, you won’t just ship features—you’ll help reinvent an industry using decades‑deep proprietary data and a modern tech stack, free from heavy legacy. Backed by full enterprise support, you’ll build reliable, explainable intelligence at real scale within a collaborative, outcome‑driven community. If you’re looking for purposeful work, constant learning, and a place where your impact and growth accelerate—this is where you’ll thrive.

Want to learn more about our Toronto Innovation Hub? Visit: (Use the "Apply for this Job" box below)./

Fitch Ratings is seeking a Lead Machine Learning Engineer to join our new AI Innovation teams in Toronto—a bold initiative building the AI‑powered future of financial analysis. We're not fine‑tuning existing models or optimizing yesterday's algorithms. We're architecting the next generation: sophisticated agentic AI systems, intelligent automation that thinks, and ML capabilities that will redefine how credit analysis happens and how global financial markets consume insights.

This is AI's moment at Fitch, and we're moving decisively. We have executive sponsorship, significant investment, and we're establishing Toronto as our AI innovation center. As a Lead ML Engineer, you'll be a technical champion driving this transformation—building breakthrough ML systems that others will study, mentoring engineers who will become tomorrow's AI leaders, and establishing patterns that will scale across the organization.

You're joining at the perfect inflection point: early enough to architect foundational decisions, resourced enough to execute boldly.

We need ML technologists who see greenfield opportunities as fuel rather than fear—whether you're an ML architect ready to design intelligent systems from first principles, an AI engineering leader who translates research breakthroughs into production reality, or a seasoned practitioner who recognizes that this moment demands courage over caution. If you're motivated by "let's prove this is possible" rather than "we need more data before we decide," this is a high‑impact leadership role where you'll spend less time justifying AI's potential and more time realizing it—alongside exceptional engineers who share your conviction that we're building something significant.

What We Offer:

Ground‑floor ML leadership with enterprise resources  – Define the ML architecture, technical standards, and engineering practices for Fitch's AI future while having the compute, research budgets, and organizational backing that most AI startups would kill for; lead and mentor a team of 4‑5 ML engineers while remaining hands‑on with the most challenging technical problems

Build breakthrough ML systems that matter  – Develop net‑new generative AI platforms, multi‑agent orchestration systems, and intelligent automation that will process billions in credit decisions; experiment with frontier models, novel architectures, and unconventional approaches; see your ML innovations directly impact how global financial markets operate

Access to cutting‑edge ML infrastructure and research  – Work with the latest LLMs, fine‑tune foundation models, leverage enterprise‑scale GPU clusters, experiment with emerging frameworks before they're mainstream, and collaborate with academic ML…