Machine Learning Engineer, Associate Director

Fitch Group • Toronto, ON

4d • Hybrid

About The Position

Fitch Ratings is seeking a Machine Learning Engineer to join our new AI Innovation teams in Toronto. This role is part of a significant initiative to build the AI-powered future of financial analysis, focusing on architecting next-generation agentic AI systems, intelligent automation, and ML capabilities that will redefine credit analysis and global financial market insights. The company is establishing Toronto as its AI innovation center with executive sponsorship and significant investment. The Lead ML Engineer will be a technical champion, building breakthrough ML systems, mentoring engineers, and establishing scalable patterns. This is an opportunity to architect foundational decisions early on and execute boldly with ample resources. The role is for ML technologists who are motivated by proving possibilities and are comfortable with greenfield opportunities. It's a high-impact role where individuals will spend less time justifying AI's potential and more time realizing it alongside exceptional engineers. This position is ideal for someone who wants to remain deeply hands-on while operating at a senior level, contributing technical expertise, influencing decision-making, and partnering with engineering leaders, product teams, and fellow engineers to deliver innovative AI solutions at scale. It is a senior individual contributor role with no direct people management responsibilities.

Requirements

• 12+ years of professional experience building production AI/ML systems, with strong proficiency in Python, ML algorithms (from classical techniques to deep learning), and modern ML frameworks; proven track record of delivering advanced generative AI and ML solutions
• Demonstrated experience designing scalable ML systems from scratch; deep understanding of ML system architecture, model deployment patterns, and the ability to make bold architectural decisions for AI platforms in ambiguous environments
• Extensive hands-on experience developing and integrating generative AI solutions, working with large language models, building agentic systems, implementing RAG architectures, and training/fine-tuning neural networks using frameworks like PyTorch
• Bachelor's degree in Machine Learning, Computer Science, Data Science, Applied Mathematics, or related field
• Deep understanding of ML operations, including containerization (Docker, Kubernetes/AWS EKS), cloud platforms (AWS/Azure), workflow orchestration (Airflow), automated testing for ML systems, and API development for model deployment
• Track record of mentoring and coaching engineers, driving ML initiatives in fast-moving environments, leading through technical excellence and influence rather than authority, and building credibility through results and vision
• Demonstrated history of exploring emerging ML technologies, building AI proofs-of-concept, learning from failures, and translating cutting-edge research into production systems; comfort with ambiguity and rapid technological change
• Ability to articulate ML technical vision to diverse audiences, work effectively with product squads and business partners, translate complex AI/ML concepts for non-technical stakeholders, and bring your whole self while remaining open to others' perspectives

Nice To Haves

• 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 with real users
• History of establishing technical direction for ML initiatives, contributing to open-source ML projects, speaking at AI/ML conferences, publishing research, or writing about practical applications of emerging AI technologies
• Hands-on experience building multi-agent systems, agentic workflows, tool-using AI systems, or complex AI orchestration platforms that go beyond simple LLM integrations
• Deep expertise building sophisticated ML infrastructure, MLOps pipelines, model serving platforms, and cloud-native AI systems at scale; experience optimizing cost and performance of production LLM deployments
• Understanding of analytical workflows, credit analysis processes, regulatory requirements, financial data products, or how ML enables better financial decision-making; familiarity with credit ratings agencies is a significant advantage
• History of building greenfield ML products, working in fast-paced AI innovation environments, or being part of 0-to-1 ML initiatives within larger organizations where you shaped technical direction
• Active participation in Toronto's AI/ML research or engineering communities, connections to academic ML research groups, or strong interest in being part of Toronto's world-class AI ecosystem

Responsibilities

• Build transformative ML systems from the ground up – Design and architect net-new generative AI solutions, agentic workflows, and intelligent platforms using advanced ML frameworks (PyTorch, etc.), large language models, and emerging AI technologies that fundamentally change how analysts work and how Fitch operates
• Drive breakthrough AI innovation and experimentation boldly – Lead exploration of generative AI, multi-agent systems, RAG architectures, model fine-tuning, prompt engineering, and other emerging ML technologies; create cutting-edge proofs-of-concept; evaluate what's transformative versus what's hype; and turn research into production-quality AI capabilities
• Define ML technical vision and architecture for the future – Shape architectural decisions for ML systems, establish ML engineering standards, drive technology and framework choices, and influence how Fitch approaches intelligent platforms and AI governance across the organization
• Lead through innovation, influence, and mentorship – Mentor and coach fellow ML engineers while partnering with product squads, business stakeholders, and cross-functional teams to translate ambitious AI ideas into elegant technical solutions; foster a culture of experimentation, continuous learning, and calculated risk-taking
• Champion ML excellence while moving fast – Balance innovation velocity with ML engineering best practices; implement robust CI/CD pipelines for ML systems; develop scalable APIs (FastAPI, etc.) for model deployment; solve novel technical challenges at the intersection of cutting-edge AI research and production systems; and build solutions that are both breakthrough and reliable
• Drive ML governance and operational excellence – Ensure adherence to AI/ML governance guidelines, monitor SLAs for AI solutions, optimize model performance and reliability, and translate complex ML concepts for both technical and non-technical audiences across distributed teams
• Shape team culture and technical direction – Help define how our AI innovation teams operate, what "good" looks like for ML engineering, and how we balance exploration with delivery; model the curiosity, boldness, and technical rigor needed to succeed in a greenfield ML innovation environment

Benefits

• Ground-floor ML leadership with enterprise resources
• Define the ML architecture, technical standards, and engineering practices for Fitch's AI future
• Compute, research budgets, and organizational backing
• Mentor and coach fellow ML engineers while remaining deeply hands-on
• Build breakthrough ML systems that matter
• Develop net-new generative AI platforms, multi-agent orchestration systems, and intelligent automation
• Experiment with frontier models, novel architectures, and unconventional approaches
• See your ML innovations directly impact how global financial markets operate
• Access to cutting-edge ML infrastructure and research
• Work with the latest LLMs, fine-tune foundation models, leverage enterprise-scale GPU clusters
• Experiment with emerging frameworks before they're mainstream
• Collaborate with academic ML researchers
• Substantial conference and training budgets
• Toronto as Fitch's AI center of excellence
• Connect with Vector Institute researchers, attend cutting-edge ML meetups
• Shape ML governance and standards for an organization
• Establish the ML engineering practices, model governance frameworks, and AI integration patterns
• Real production impact with sophisticated ML challenges
• Build ML systems that analysts and financial professionals actually use daily
• Solve hard problems at the intersection of NLP, document intelligence, reasoning systems, and production-scale deployment
• Measure your impact in both model performance and business outcomes
• Accelerated career trajectory in AI leadership
• High visibility to C-suite executives
• Clear advancement paths to Principal ML Architect or AI Research Lead roles
• Opportunity to establish yourself as a recognized voice in financial AI

What This Job Offers

• Job Type: Full-time
• Career Level: Senior