Senior Machine Learning Engineer

Fitch Group•Toronto, ON

49d•Hybrid

About The Position

Fitch Ratings is seeking a Senior Machine Learning Engineer to join its new AI Innovation teams in Toronto, focusing on building the AI-powered future of financial analysis. This role involves designing and shipping generative AI systems, agentic workflows, and intelligent platforms to transform credit analysis and global financial markets. The company is making a significant strategic investment in AI, with Toronto serving as its innovation center. The Senior ML Engineer will be a technical leader, building sophisticated ML systems, driving innovation, mentoring engineers, and establishing technical standards. The role offers a greenfield opportunity to shape how AI systems are built with enterprise backing and resources.

Requirements

6+ years of professional experience building production AI/ML systems.
Demonstrated ability to deliver advanced generative AI and ML solutions from concept through deployment.
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 systems.
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 scalability.
Bachelor's degree in Machine Learning, Computer Science, Data Science, Applied Mathematics, or related field.
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 systems.
Deep understanding of automated testing, source version control, code optimization, software architecture, and building scalable, maintainable systems.
Track record of leading project initiatives, mentoring team members, shaping technical strategy without direct management, and driving innovation in fast-paced environments.
Ability to work effectively with technical and non-technical stakeholders, translate complex ML concepts for diverse audiences, and foster alignment across distributed cross-functional teams.

Nice To Haves

Master's or PhD is strongly preferred.
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.
Hands-on experience building sophisticated multi-agent systems, agentic workflows, tool-using AI, or complex AI orchestration platforms that demonstrate advanced reasoning and autonomy.
Advanced expertise building ML infrastructure, sophisticated MLOps pipelines, model serving platforms, and optimizing cost/performance of production LLM deployments at scale.
Experience developing or integrating ML functionality for document management systems, content platforms, or document intelligence solutions.
Contributions to open source ML projects, conference presentations, published research, blog posts about practical ML applications, or active participation in the ML engineering community.
Understanding of credit analysis workflows, regulatory requirements, financial data products, or how ML enables better financial decision-making; familiarity with ratings agencies is valuable.
History of building greenfield ML products, working in fast-paced AI innovation teams, or being part of early-stage initiatives where you helped shape technical direction and culture.
Active participation in Toronto's AI/ML engineering or research communities, connections to academic groups, or strong interest in contributing to Toronto's world-class AI ecosystem.

Responsibilities

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 performs.
Experiment with frontier models, implement novel ML architectures, evaluate emerging AI technologies, build proofs-of-concept, and translate cutting-edge research into production capabilities.
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 reliable.
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 excellence.
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 workflows.
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 systems.
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.
Ensure seamless transitions from prototype to production.
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 expertise and experimentation.