Machine Learning Engineer, Associate Director (Chicago)

Machine Learning Engineer, Associate Director (Chicago)

Location: Chicago, IL, US

Fitch Ratings is seeking a Machine Learning Engineer to join our new AI Innovation teams in Chicago—a bold initiative building the AI-powered future of financial analysis. We're not fine-tuning existing models or optimizing yesterday's algorithms. We're architecting the next generation: sophisticated agentic AI systems, intelligent automation that thinks, and ML capabilities that will redefine how credit analysis happens and how global financial markets consume insights.

This is AI's moment at Fitch, and we're moving decisively. As a Lead ML Engineer, you'll be a technical champion driving this transformation—building breakthrough ML systems that others will study, mentoring engineers who will become tomorrow's AI leaders, and establishing patterns that will scale across the organization. You're joining at the perfect inflection point: early enough to architect foundational decisions, resourced enough to execute boldly.

We need ML technologists who see greenfield opportunities as fuel rather than fear—whether you're an ML architect ready to design intelligent systems from first principles, an AI engineering leader who translates research breakthroughs into production reality, or a seasoned practitioner who recognizes that this moment demands courage over caution. If you're motivated by "let's prove this is possible" rather than "we need more data before we decide," this is a high-impact role where you'll spend less time justifying AI's potential and more time realizing it—alongside exceptional engineers who share your conviction that we're building something significant.

This role is ideal for someone who wants to remain deeply hands‑on while operating at a senior level. You will contribute technical expertise, influence decision-making through strong engineering judgment, and partner closely with engineering leaders, product teams, and fellow engineers to deliver innovative AI solutions at scale. This is a senior individual contributor role with no direct people management responsibilities

What We Offer:

• Ground-floor ML leadership with enterprise resources – Define the ML 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

We'll Count on You To:

• 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