Machine Learning Engineer, Associate Director (Chicago)

About Fitch Group Fitch Group is a global leader in financial information services with operations in more than 30 countries. Wholly owned by Hearst, Fitch Group is comprised of: Fitch Ratings, a global leader in credit ratings and research and Fitch Solutions, a leading provider of data, research and analytics. With dual headquarters in London and New York, Fitch Group is owned by Hearst.

Fitch Group

F

fitch.group

5,001-10,000 employees

Financial Services

Canary Wharf, London

Privately Held

26k LinkedIn followers

Required

• Deep ML technical expertise – 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
• ML architectural mastery and greenfield experience – 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
• Advanced generative AI expertise – 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
• Production ML engineering excellence – 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
• Technical leadership and influence – 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
• Innovation and experimentation mindset – 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
• Outstanding collaboration and communication – 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

Preferred

• Cutting-edge AI research to production experience – 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
• ML thought leadership and technical strategy – 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
• Multi-agent and agentic systems expertise – Hands-on experience building
• multi-agent systems, agentic workflows, tool-using AI systems, or complex AI
• orchestration platforms that go beyond simple LLM integrations
• Advanced cloud-native ML infrastructure – 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
• Financial services or analytical domain knowledge – 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
• Startup or innovation team experience – 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
• Toronto AI/ML community connection – 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
• If you're ready to build transformative ML systems with organizational backing,
• talented colleagues, and the resources to succeed—this is the moment to join us.
• differentiates us. Our global network of colleagues comes together to accomplish
• things greater than they ever could alone.
• Every team member is essential to our business, and each perspective is critical
• to our success. We embrace a diverse culture that encourages a free exchange of
• ideas, guaranteeing your voice will be heard and your work will have an impact,
• regardless of seniority.
• We are building incredible things at Fitch and we invite you to join us on our
• journey.
• Fitch Group is a global leader in financial information services with operations
• in more than 30 countries. Wholly owned by the Hearst Corporation, we are
• comprised of three main businesses: Fitch Ratings | Fitch Solutions | Fitch
• Learning.
• Fitch is committed to providing global securities markets with objective,
• timely, independent and forward-looking credit opinions. To protect Fitch's
• credibility and reputation, our employees must take every precaution to avoid
• conflicts of interests or any appearance of a conflict of interest. Should you
• be successful in the recruitment process at Fitch Ratings you will be asked to
• declare any securities holdings and other potential conflicts prior to
• commencing employment. If you, or your immediate family, have any holdings that
• may conflict with your work responsibilities, you may be asked to divest
• yourself of them before beginning work.
• Fitch Group is proud to be an Equal Opportunity and Affirmative Action Employer.
• FOR CHICAGO ROLES ONLY: Expected base pay rates for the role will be between
• 140,000 USD and 180,000 USD. Actual salaries will be determined on an
• individualized basis and may vary based on factors including but not limited to
• education, training, experience, past performance, and other job-related
• factors. Base pay is one part of Fitch’s total compensation package, which,
• depending on the position, may also include commission earnings, discretionary
• bonuses, long-term incentives, and other benefits sponsored by Fitch.
• Please note that this is a hybrid role and is not fully remote. The successful
• candidate will be expected to work onsite 2–3 days per week in our Chicago
• office.

• architecture, technical standards, and engineering practices for Fitch's AI
• future while having the compute, research budgets, and organizational backing
• that most AI startups would envy; mentor and coach fellow ML engineers while
• remaining deeply hands-on with the most challenging technical problems
• Build breakthrough ML systems that matter – Develop net-new generative AI
• platforms, multi-agent orchestration systems, and intelligent automation that
• will process billions in credit decisions; 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, and
• collaborate with academic ML researchers; substantial conference and training
• budgets to stay at the forefront of AI innovation
• Shape ML governance and standards for an organization – Establish the ML
• engineering practices, model governance frameworks, and AI integration
• patterns that will guide Fitch's AI transformation; your architectural
• decisions will influence how a global financial services leader approaches
• intelligent systems
• 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 making billion-dollar strategic decisions; clear advancement paths
• to Principal ML Architect or AI Research Lead roles; opportunity to establish
• yourself as a recognized voice in financial AI and earn a reputation that
• opens doors across the industry
• 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