Machine Learning Engineer, Associate Director (Chicago)

Fitch Group•Chicago, IL

1d•$140,000 - $175,000•Hybrid

About The Position

Fitch Ratings is seeking a Machine Learning Engineer to join our new AI Innovation teams in Chicago. This role is part of a significant initiative to build AI-powered financial analysis tools. The focus is on architecting next-generation systems, including agentic AI, intelligent automation, and advanced ML capabilities that will redefine credit analysis and market insights. The position offers an opportunity to be a technical leader in this transformation, building impactful ML systems, mentoring engineers, and establishing scalable practices. It's an early-stage role with substantial resources, allowing for bold execution and foundational decision-making. The ideal candidate is motivated by proving possibilities and is comfortable with greenfield opportunities. This is a senior individual contributor role with no direct people management responsibilities, focused on hands-on technical contribution and influencing decision-making.

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