Machine Learning Engineer, Associate Director

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 mentorship – Mentor and coach fellow 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

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

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

Technical leadership and influence – Track record of mentoring and coaching engineers, driving ML initiatives in fast-moving environments, leading through technical excellence and influence rather than authority, 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

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.

At Fitch Group, the combined power of our global perspectives is what differentiates us. Our global network of colleagues comes together to accomplish things greater than they ever could alone.

Every team member is essential to our business, and each perspective is critical to our success. We embrace a diverse culture that encourages a free exchange of ideas, guaranteeing your voice will be heard and your work will have an impact, regardless of seniority.

We are building incredible things at Fitch and we invite you to join us on our journey.

Fitch Group is a global leader in financial information services with operations in more than 30 countries. Wholly owned by the Hearst Corporation, we are comprised of three main businesses: Fitch Ratings | Fitch Solutions | Fitch Learning.

For more information please visit our websites: www.fitchratings.com | www.fitchsolutions.com | www.fitchlearning.com

Fitch is committed to providing global securities markets with objective, timely, independent and forward-looking credit opinions. To protect Fitch's credibility and reputation, our employees must take every precaution to avoid conflicts of interests or any appearance of a conflict of interest. Should you be successful in the recruitment process at Fitch Ratings you will be asked to declare any securities holdings and other potential conflicts prior to commencing employment. If you, or your immediate family, have any holdings that may conflict with your work responsibilities, you may be asked to divest yourself of them before beginning work.

Fitch Group is proud to be an Equal Opportunity and Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.

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