Senior Engineer, Applied Artificial Intelligence
Chicago, IL Hybrid
$140,250–$181,500 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
Building trusted markets — powered by our people At Cboe Global Markets, we inspire our people to solve complex challenges together because what we do matters. We provide the financial infrastructure that powers the global economy. As a leading provider of market infrastructure and tradable products, Cboe delivers cutting-edge trading, clearing and investment solutions to market participants around the world. We’re building meaningful ways to support professional and personal development while strengthening the trust we’ve earned as a global market leader. Our teams are empowered to share ideas, actively pursue them and bring on a challenge. As champions of internal mobility and access to opportunity, we encourage our people to “go for it” and equip our managers with the training to coach their teams to the next level. We strive to provide employees a safe space to network, share ideas and create opportunities. To support strong partnership and team connection, this role follows a four day in office work model. As a Senior AI Engineer, you will play a central role in integrating AI into our core operations and developing AI-native products. You'll own the full lifecycle of AI initiatives from sitting with business stakeholders to scope and discover opportunities through to hands-on design, development, and deployment of production-grade systems. You won't just be building agents; you'll develop and define reusable services for agents across Cboe.
What you'll bring
- 7–10+ years of relevant professional experience in software engineering, with increasing seniority
- 3+ years of professional experience in applied AI (building agents, MCP servers, context/harness engineering)
- Proven experience building and deploying AI solutions in production environments, including familiarity with agent frameworks and a clear understanding of what they do and why they matter
- Hands-on experience building tools and integrations for LLM-based systems
- Deep proficiency in context engineering including prompt design, retrieval strategies, and memory/context management
- Demonstrated ability to work directly with non-technical stakeholders to scope AI opportunities and define technical requirements
- Strong programming skills
- Experience with LLM evaluation, observability, and benchmarking as core engineering practice
- Ability to work independently, manage ambiguity, and deliver results without close oversight
- Track record of setting technical standards, leading design/architecture reviews, and mentoring engineers.
- This role carries a formal technical leadership expectation
- Strong written and verbal communication skills, particularly the ability to explain complex AI concepts to non-technical audiences