Machine Learning Engineer, Associate Director

About the position

Fitch Ratings is seeking a 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 maintaining existing models or running experiments in isolation. This is about building and shipping real generative AI systems, agentic workflows, and intelligent platforms that will transform how credit analysis happens and fundamentally change how global financial markets operate.\nWe're at a pivotal moment. Fitch is making a major strategic bet on AI, investing heavily in Toronto as our innovation center, and we're building teams of talented ML engineers to turn ambitious vision into production reality. As an ML Engineer, you'll be hands-on building sophisticated ML systems, working directly with cutting-edge technologies, learning from exceptional senior engineers, and contributing to solutions that will have measurable impact. This is your opportunity to accelerate your ML career by working on real problems that matter, with the resources and mentorship to grow rapidly.\nWe need ML engineers who are excited about greenfield opportunities and eager to learn—whether you're passionate about working with LLMs, excited to build intelligent systems that reason and act, or energized by turning AI research into production code. If you're motivated by \"let me build this and learn what's possible\" rather than \"let me wait to be told exactly what to do,\" this is a high-growth role where you'll ship transformative ML systems—working alongside talented engineers who will help you level up your skills while building something significant.

Responsibilities

• Build and deploy production ML systems – Develop generative AI solutions, agentic workflows, and intelligent platforms using Python, PyTorch, modern ML frameworks, and large language models; write high-quality, production-ready code that scales and performs
• Implement AI solutions in collaboration with product teams – Work closely with or as part of product squads to integrate ML capabilities into flagship Fitch products and workflows; share best practices and learnings with cross-functional team members
• Develop scalable ML infrastructure and workflows – Build robust APIs (FastAPI, etc.) for model deployment, implement data pipelines using orchestration platforms (Airflow), leverage cloud services (AWS/Azure) for ML infrastructure, and create software artifacts that integrate diverse data formats into dynamic ML systems
• Support and improve production ML solutions – Help maintain SLAs for AI applications, use metrics to evaluate and guide improvements to existing ML solutions, monitor model performance, and contribute to the reliability and effectiveness of production systems
• Experiment with emerging AI technologies – Explore generative AI frameworks, work with LLMs, implement RAG architectures, experiment with agentic workflows, and help evaluate which emerging technologies deliver real value versus hype
• Collaborate effectively across teams – Communicate ML concepts to diverse stakeholders, work with data scientists to identify innovative solutions, partner with senior engineers to design scalable architectures, and contribute to seamless integration of AI into broader workflows
• Champion quality and best practices – Adhere to software and ML development fundamentals including code quality, automated testing, source version control, optimization, and containerization (Docker, Kubernetes/AWS EKS); learn and apply architectural best practices
• Learn, grow, and contribute to team culture – Actively seek feedback, embrace mentorship, share learnings with the team, experiment boldly, learn from failures, and contribute to a culture of curiosity, innovation, and technical excellence

Requirements

• Solid ML engineering foundation – 3+ years of professional experience as an AI/ML engineer building production-quality solutions; demonstrated ability to deliver ML systems from development through deployment
• Strong Python development skills – Experience developing production-quality Python code with strong adherence to software development fundamentals (code quality, automated testing, source version control, optimization)
• Generative AI and LLM experience – Hands-on experience building generative AI frameworks, working with large language models, leveraging and/or fine-tuning LLMs; experience building agentic workflows strongly preferred
• ML algorithm proficiency – Working knowledge of ML algorithms including multi-class classification, decision trees, support vector machines, and neural networks (deep learning experience strongly preferred)
• Cloud platform knowledge – Practical knowledge of AWS and Azure infrastructure and services (e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage); ability to leverage cloud services for ML infrastructure and LLM workflows
• Experience integrating AI solutions – Track record of integrating AI and ML solutions into existing workflows, products, and systems; ability to work collaboratively to ensure seamless deployment
• Search and information retrieval experience – Experience building or enhancing search systems and information retrieval capabilities; understanding of how to make information discoverable and accessible
• Containerization exposure – Experience or strong familiarity with containerization technologies like Docker, Kubernetes, AWS EKS for building scalable ML systems
• Bachelor's degree in Machine Learning, Computer Science, Data Science, Applied Mathematics, or related technical field (Master's or higher strongly preferred)

Nice-to-haves

• Advanced agentic workflow experience – Hands-on experience building sophisticated agentic workflows powered by language models; understanding of multi-agent systems and AI orchestration patterns
• Document and content systems experience – Experience developing or integrating ML functionality for document management systems, content platforms, or document intelligence solutions
• Prototype-to-production experience – Experience supporting prototyping teams and enabling seamless transitions from experimental proof-of-concept to production deployment; ability to bridge the gap between research and engineering
• Strong collaboration and communication skills – Proven ability to work effectively in distributed team environments, communicate technical concepts clearly, collaborate with non-AI/ML teams, and work efficiently in fast-paced settings
• Cross-functional team experience – Track record of working successfully with product managers, business stakeholders, and engineers from different disciplines to integrate AI solutions into broader workflows and projects
• Full-stack or polyglot programming – Experience working in Java and/or JavaScript codebases in addition to Python; ability to integrate ML solutions into diverse technology stacks
• Financial services knowledge – Familiarity with credit ratings agencies, regulatory requirements, financial data products, or analytical workflows; understanding of how ML can enhance financial decision-making
• Passion for ML-driven outcomes – Genuine enthusiasm for using data and ML to drive better business outcomes; demonstrated curiosity about emerging AI technologies and their practical applications
• Code quality advocacy – Strong advocate of good code quality and architectural practices; commitment to writing maintainable, tested, well-documented code
• Toronto AI/ML community interest – Interest in participating in Toronto's AI/ML engineering or research communities, attending meetups, or contributing to Toronto's world-class AI ecosystem

Benefits

• Hands-on experience with cutting-edge ML technology
• Build real ML systems with measurable impact
• Accelerate your ML career
• Toronto's world-class AI ecosystem
• Greenfield innovation with enterprise backing
• Continuous learning and growth
• High visibility and clear growth path