Lead Machine Learning Engineer - AI Innovation Teams

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 mindset - Demonstrated history of exploring emerging ML technologies, building AI proofs-of-concept, learning from failures, and translating cutting-edge research into production systems; comfort with ambiguity and rapid technological change
• Outstanding collaboration and communication - Ability to articulate ML technical vision to diverse audiences, work effectively with product squads and business partners, translate complex AI/ML concepts for non-technical stakeholders, and bring your whole self while remaining open to others' perspectives

What Would Make You Stand Out:

• Cutting-edge AI research to production experience - Track record of taking breakthrough AI capabilities from research/prototype to production-scale deployment; experience supporting seamless transitions from experimentation to enterprise-grade ML systems with real users
• ML thought leadership and technical strategy - History of establishing technical direction for ML initiatives, contributing to open source ML projects, speaking at AI/ML conferences, publishing research, or writing about practical applications of emerging AI technologies
• Multi-agent and agentic systems expertise - Hands-on experience building multi-agent systems, agentic workflows, tool-using AI systems, or complex AI orchestration platforms that go beyond simple LLM integrations
• Advanced cloud-native ML infrastructure - Deep expertise building sophisticated ML infrastructure, MLOps pipelines, model serving platforms, and cloud-native AI systems at scale; experience optimizing cost and performance of production LLM deployments
• Financial services or analytical domain knowledge - Understanding of analytical workflows, credit analysis processes, regulatory requirements, financial data products, or how ML enables better financial decision-making; familiarity with credit ratings agencies is a significant advantage
• Startup or innovation team experience - History of building greenfield ML products, working in fast-paced AI innovation environments, or being part of 0-to-1 ML initiatives within larger organizations where you shaped technical direction
• Toronto AI/ML community connection - Active participation in Toronto's AI/ML research or engineering communities, connections to academic ML research groups, or strong interest in being part of Toronto's world-class AI ecosystem

If you're ready to build transformative ML systems with organizational backing, talented colleagues, and the resources to succeed-this is the moment to join us.

FOR TORONTO ROLES ONLY: Expected base pay rates for the role will be between 200,000 CAD and 250,000 CAD. Actual salaries will be det