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: https://careers.fitch.group/go/Toronto-Innovation-Hub/9713801/

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 researchers; substantial conference and training budgets to stay at the forefront of AI innovation

Toronto as Fitch's AI center of excellence

– Join our strategic investment in Toronto—one of the world’s premier AI research hubs—where you'll connect with Vector Institute researchers, attend cutting‑edge ML meetups, and be part of the ecosystem that's defining the future of applied AI

Shape ML governance and standards for an organization

– Establish the ML engineering practices, model governance frameworks, and AI integration patterns that will guide Fitch's AI transformation; your architectural decisions will influence how a global financial services leader approaches intelligent systems

Real production impact with sophisticated ML challenges

– Build ML systems that analysts and financial professionals actually use daily; solve hard problems at the intersection of NLP, document intelligence, reasoning systems, and production‑scale deployment; measure your impact in both model performance and business outcomes

Accelerated career trajectory in AI leadership

– High visibility to C‑suite executives making billion‑dollar strategic decisions; clear advancement paths to Principal ML Architect or AI Research Lead roles; opportunity to establish yourself as a recognized voice in financial AI and earn a reputation that opens doors across the industry

We'll Count on You To:

Build transformative ML systems from the ground up

– Design and architect net‑new generative AI solutions, agentic workflows, and intelligent platforms using advanced ML frameworks (PyTorch, etc.), large language models, and emerging AI technologies that fundamentally change how analysts work and how Fitch operates

Drive breakthrough AI innovation and experimentation boldly

– Lead exploration of generative AI, multi‑agent systems, RAG architectures, model fine‑tuning, prompt engineering, and other emerging ML technologies; create cutting‑edge proofs‑of‑concept; evaluate what's transformative versus what's hype; and turn research into production‑quality AI capabilities

Define ML technical vision and architecture for the future

– Shape architectural decisions for ML systems, establish ML engineering standards, drive technology and framework choices, and influence how Fitch approaches intelligent platforms and AI governance across the organization

Lead through innovation, influence, and people management

– Manage and mentor a team of 4‑5 ML engineers while partnering with product squads, business stakeholders, and cross‑functional teams to translate ambitious AI ideas into elegant technical solutions; foster a culture of experimentation, continuous learning, and calculated risk‑taking

Champion ML excellence while moving fast

– Balance innovation velocity with ML engineering best practices; implement robust CI/CD pipelines for ML systems; develop scalable APIs (FastAPI, etc.) for model deployment; solve novel technical challenges at the intersection of cutting‑edge AI research and production systems; and build solutions that are both breakthrough and reliable

Drive ML governance and operational excellence

– Ensure adherence to AI/ML governance guidelines, monitor SLAs for AI solutions, optimize model performance and reliability, and translate complex ML concepts for both technical and non‑technical audiences across distributed teams

Shape team culture and technical direction

– Help define how our AI innovation teams operate, what "good" looks like for ML engineering, and how we balance exploration with delivery; model the curiosity, boldness, and technical rigor needed to succeed in a greenfield ML innovation environment

What You Need to Have:

Deep ML technical expertise

– 12+ years of professional experience building production AI/ML systems, with strong proficiency in Python, ML algorithms (from classical techniques to deep learning), and modern ML frameworks; proven track record of delivering advanced generative AI and ML solutions

ML architectural mastery and greenfield experience

– Demonstrated experience designing scalable ML systems from scratch; deep understanding of ML system architecture, model deployment patterns, and the ability to make bold architectural decisions for AI platforms in ambiguous environments

Advanced generative AI expertise

– Extensive hands‑on experience developing and integrating generative AI solutions, working with large language models, building agentic systems, implementing RAG architectures, and training/fine‑tuning neural networks using frameworks like PyTorch

Bachelor's degree in Machine Learning, Computer Science, Data Science, Applied Mathematics, or related field

(Master's or PhD is strongly preferred)

Production ML engineering excellence

– Deep understanding of ML operations, including containerization (Docker, Kubernetes/AWS EKS), cloud platforms (AWS/Azure), workflow orchestration (Airflow), automated testing for ML systems, and API development for model deployment

Leadership and people management

– Track record of managing and mentoring technical teams, driving ML initiatives in fast‑moving environments, balancing hands‑on technical contributions with team leadership, and building credibility through results and vision

Innovation and experimentation mi...