Machine Learning Engineer - AI Innovation Teams

Fitch Ratings is seeking a Machine Learning Engineer to join our AI Innovation teams in Toronto. This role focuses on building and shipping generative AI systems, agentic workflows, and intelligent platforms that transform credit analysis.

We are at a pivotal moment, investing heavily in Toronto as our innovation center. As an ML Engineer you’ll build sophisticated ML systems, learn from senior engineers, and contribute solutions with measurable impact.

• 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.
• Build real ML systems with measurable impact – Develop production generative AI capabilities, intelligent automation, and ML solutions used daily by analysts and financial professionals.
• Accelerate your ML career – Work alongside senior ML engineers and technical leaders who mentor and review code, and provide opportunities to take on increasing responsibility.
• Toronto’s world‑class AI ecosystem – Participate in AI meetups, conferences, and connect with the Vector Institute researchers.
• Greenfield innovation with enterprise backing – Build net‑new ML systems from scratch with freedom to experiment, backed by compute resources, training budgets, and organizational support.
• Continuous learning and growth – Conference attendance, training budgets, access to latest research and tools, and a culture that values experimentation.
• High visibility and clear growth path – Contribute to high‑impact projects with visibility to senior leadership and opportunities to advance to Senior ML Engineer.

• Build and deploy production ML systems – Develop generative AI solutions, agentic workflows, and intelligent platforms using Python, PyTorch, and large language models; write high‑quality, production‑ready code.
• Implement AI solutions in collaboration with product teams – Integrate ML capabilities into flagship Fitch products and workflows; share best practices with cross‑functional team members.
• Develop scalable ML infrastructure and workflows – Build robust APIs (FastAPI), implement data pipelines with Airflow, leverage cloud services (AWS/Azure) for ML infrastructure, and integrate diverse data formats.
• Support and improve production ML solutions – Maintain SLAs, use metrics to evaluate and improve models, monitor performance, and ensure reliability.
• Experiment with emerging AI technologies – Explore new frameworks, work with LLMs, implement RAG architectures, and assess value versus hype.
• Collaborate effectively across teams – Communicate ML concepts, partner with data scientists, senior engineers, and stakeholders to design scalable architectures.
• Champion quality and best practices – Follow code quality, automated testing, version control, optimization, and containerization (Docker, Kubernetes, AWS EKS).
• Learn, grow, and contribute to team culture – Seek feedback, embrace mentorship, share knowledge, and promote curiosity, innovation, and technical excellence.

• Solid ML engineering foundation – 3+ years of professional experience building production‑quality ML solutions.
• Strong Python development skills – Production‑ready Python code with adherence to software fundamentals.
• Generative AI and LLM experience – Hands‑on work with generative AI frameworks, large language models, and agentic workflows is strongly preferred.
• ML algorithm proficiency – Knowledge of classification, decision trees, SVMs, and neural networks; deep learning experience preferred.
• Cloud platform knowledge – Practical use of AWS and Azure services (e.g., Bedrock, Sage Maker, Azure AI Search, blob storage).
• Experience integrating AI solutions – Proven ability to embed AI/ML into existing workflows, products, and systems.
• Search and information retrieval experience – Building or enhancing search systems and information retrieval capabilities.
• Containerization exposure – Familiarity with Docker, Kubernetes, AWS EKS for scalable ML systems.
• Bachelor’s degree in Machine Learning, Computer Science, Data…