Machine Learning Engineer, Associate Director

Machine Learning Engineer – AI Innovation Teams Location: Toronto 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. We'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. We 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. What We Offer: • Hands-on experience with cutting-edge ML technology – Work directly with the latest LLMs and foundation models, implement RAG architectures, build agentic systems, fine-tune neural networks, and leverage enterprise-scale GPU clusters and cloud infrastructure; learn from senior ML engineers who are at the forefront of applied AI • Build real ML systems with measurable impact – Develop production generative AI capabilities, intelligent automation, and ML solutions that analysts and financial professionals use daily; see your code directly contribute to systems that will process billions in credit decisions and change how financial markets operate • Accelerate your ML career – Work alongside senior ML engineers and technical leaders who will mentor you, review your code, and help you grow; exposure to architectural decisions, technical strategy discussions, and the opportunity to take on increasing responsibility as you demonstrate your capabilities • Toronto's world-class AI ecosystem – Be part of one of the world's premier AI research hubs, attend cutting-edge ML meetups and conferences, connect with Vector Institute researchers, and immerse yourself in the community defining the future of applied AI and machine learning • Greenfield innovation with enterprise backing – Build net-new ML systems from scratch with the freedom to experiment and learn, backed by compute resources, training budgets, and organizational support that enable you to focus on building breakthrough AI rather than fighting for resources • Continuous learning and growth – Conference attendance, training budgets, access to the latest research and tools, and a culture that values experimentation and learning from failures; work on diverse problems that will rapidly expand your ML expertise • High visibility and clear growth path – Contribute to high-impact projects with visibility to senior leadership; clear advancement opportunities to Senior ML Engineer roles as you develop your skills and demonstrate impact We'll Count on You To: • 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 What You Need to Have: • 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) What Would Make You Stand Out: • Advanced agentic workflow experience – Hands-on experience building sophisticated agentic workflows powered by language models; understanding of multi-agent systems and AI or