Senior Machine Learning Engineer

Senior 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: Ratings is seeking a Senior Machine Learning Engineer to join our new AI Innovation teams in Toronto—where we're building the AI-powered future of financial analysis from the ground up. This isn't about tweaking hyperparameters or maintaining legacy models. This is about designing and shipping breakthrough generative AI systems, agentic workflows, and intelligent platforms that will transform how credit analysis happens and how global financial markets operate.We're at a defining moment. Fitch is making a major strategic bet on AI, investing heavily in Toronto as our innovation center, and we're building teams of exceptional ML engineers to turn ambitious vision into production reality. As a Senior ML Engineer, you'll be a technical force on the team—building sophisticated ML systems, driving innovation through hands‑on work, mentoring engineers, and helping establish the technical standards that will scale across the organization. You're joining early enough to shape how we build, with the resources and runway to do it right.We need ML engineers who thrive on hard problems and greenfield opportunities—whether you're passionate about pushing the boundaries of what LLMs can do, excited to architect intelligent systems that reason and act, or energized by turning cutting‑edge research into production capabilities. If you're motivated by "let's build this and see what's possible" rather than "let's wait and see what others do," this is a high‑impact role where you'll spend your time shipping transformative ML systems—working alongside talented engineers who are equally committed to building something significant.What We Offer:Build breakthrough ML systems with real impact
– Design and ship production generative AI platforms, multi‑agent systems, and intelligent automation that will process billions in credit decisions;
work on problems at thefrontier of applied AI while seeing your work directly change how analysts and financial markets operateWork at the cutting edge of ML technology
– Experiment with the latest LLMs and foundation models, implement novel RAG architectures, build agentic systems, fine‑tune neural networks, and leverage enterprise‑scale GPU clusters and cloud infrastructure;
substantial conference and training budgets to stay at the forefrontTechnical leadership without the bureaucracy
– Lead projects, mentor junior engineers, shape technical direction, and influence architectural decisions through your expertise and results—not through management hierarchy;
your ideas and code will define how we build AI systemsToronto's world‑class AI ecosystem
– Work in one of the world's premier AI research hubs alongside Vector Institute researchers, attend cutting‑edge ML meetups and conferences, and be part of the community defining the future of applied AI and machine learningGreenfield innovation with enterprise backing
– Build net‑new ML systems from scratch with the freedom to experiment boldly, fail fast, and push boundaries—backed by the compute resources, research budgets, and organizational support that most startups can only dream ofSolve sophisticated ML challenges
– Tackle hard problems at the intersection of NLP, document intelligence, reasoning systems, agentic workflows, and production‑scale deployment;
work on challenges thatwill expand your expertise and push your technical boundariesClear growth trajectory
– High visibility to senior leadership, mentorship from experienced ML architects, and clear paths to Lead ML Engineer or Principal ML Engineer roles;
build a reputation as ago‑to expert in generative AI and financial technologyWe'll Count on You To:
Design and build transformative ML systems
– Lead the development of advanced generative AI solutions, agentic workflows, RAG architectures, and intelligent platforms using PyTorch, modern ML frameworks, and large language models;
write production‑quality code that scales and performsDrive AI innovation through hands‑on technical work
– Experiment with frontier models, implement novel ML architectures, evaluate emerging AI technologies, build proofs‑of‑concept, and translate cutting‑edge research into production capabilities that deliver real business valueLead projects and drive technical excellence
– Take ownership of significant ML initiatives from design through deployment;
make architectural decisions, establish coding standards, implement robust CI/CD for ML systems, and ensure solutions are both innovative and reliableMentor and elevate junior engineers
– Provide technical guidance, conduct code reviews, share ML best practices, and help junior team members grow their skills;
foster a culture of learning, experimentation, and technical excellenceBuild production ML infrastructure
– Develop scalable APIs (FastAPI, etc.) for model deployment, implement MLOps pipelines, leverage cloud platforms (AWS/Azure) to optimize AI infrastructure, and use orchestration tools (Airflow) for complex ML workflowsChampion ML governance and operational excellence
– Ensure adherence to AI/ML governance guidelines, monitor model performance and SLAs, optimize systems for reliability and cost‑effectiveness, and implement best practices for production ML systemsCollaborate across teams effectively
– Partner with product squads, business stakeholders, and cross‑functional teams to integrate ML solutions into flagship products and workflows;
translate complex ML concepts for diverse audiences;
and ensure seamless transitions from prototype to productionStay at the bleeding edge
– Continuously explore emerging ML technologies, attend conferences, contribute insights from research, and bring innovative approaches back to the team;
help shape our technical strategy through your expertise and experimentationWhat You Need to Have:
Strong ML engineering expertise
– 6+ years of professional experience building production AI/ML systems, with demonstrated ability to deliver advanced generative AI and ML solutions from concept through deploymentAdvanced generative AI and LLM experience
– Extensive hands‑on experience developing and integrating generative AI solutions, working with large language models, building agentic workflows, implementing RAG architectures, and integrating AI capabilities into existing products and systemsDeep ML technical skills
– Strong proficiency in Python and ML algorithms ranging from classical techniques to deep learning;
proven experience training, fine‑tuning, and deploying neural network models using PyTorch with focus on performance optimization and scalabilityBachelor'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
– Strong understanding of MLOps, containerization (Docker, Kubernetes/AWS EKS), cloud platforms (AWS/Azure including Bedrock, SageMaker, Azure AI Search), workflow orchestration (Airflow), and API development for ML systemsSoftware development fundamentals
– Deep understanding of automated testing, source version control, code optimization, software architecture, and building scalable, maintainable systemsTechnical leadership through influence
– Track record of leading project initiatives, mentoring team members, shaping technical strategy without direct management, and driving innovation in fast‑paced environmentsOutstanding collaboration and communication
– Ability to work effectively with technical and non‑technical stakeholders, translate complex ML concepts for diverse audiences, and foster alignment across distributed cross‑functional teamsWhat Would Make You Stand Out:
Cutting‑edge AI implementation 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 systemsMulti‑agent and agentic systems expertise
– Hands‑on experience building sophisticated multi‑agent systems, agentic workflows, tool‑using AI, or complex AI orchestration platforms that demonstrate advanced reasoning and autonomyMLOps and cloud‑native infrastructure
– Advanced expertise building ML infrastructure, sophisticated MLOps pipelines, model serving platforms, and optimizing cost/performance of production LLM deployments at scaleDocument and content management systems experience
– Experience developing or integrating ML functionality for document management systems, content platforms, or document intelligence solutionsTechnical thought leadership
– Contributions to open source ML projects, conference presentations, published research, blog posts about practical ML applications, or active participation in the ML engineering communityFinancial services or analytical domain knowledge
– Understanding of credit analysis workflows, regulatory requirements, financial data products, or how ML enables better financial decision‑making;
familiarity with ratings agencies is valuableStartup or innovation environment experience
– History of building greenfield ML products
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