Senior Engineer, Applied Artificial Intelligence

About this role

Job Description: 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.

Location Overview Cboe HQ is located in the historic Old Post Office district, it’s a landmark that blends classic architecture with modern amenities. The building features expansive spaces with high ceilings and large windows, offering an abundance of natural light and panoramic views of the city skyline and the Chicago River.

With its prime location in the heart of downtown, the OPO Building provides easy access to major transportation hubs, including Union Station and multiple CTA lines, making it convenient for commuters. The building is home to a variety of amenities, including restaurants, a fitness center, and collaborative workspaces, creating a vibrant and dynamic work environment in one of Chicago's most iconic areas.

Role Overview 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.

This is a high-ownership, high-impact role for someone who wants to operate independently, translate organizational strategy into concrete technical outcomes, and bring others along with them. You'll be expected to work independently and consider the impact of technology on strategy. You'll bring both deep technical expertise and strong communication skills. In addition to pushing our AI initiatives forward, you'll mentor other engineers on the team and help establish the standard for how we build AI at Cboe.

Your responsibilities will be:

• Design, build, and deploy production-ready AI agents and agentic systems across Cboe's internal platforms and workflows. • Own scoping and discovery for AI initiatives — partnering directly with non-technical business stakeholders to understand needs and convert them into well-defined technical solutions. • Build and enhance Cboe's agentic control plane. • Apply context engineering principles (prompt design, memory architecture, retrieval strategy) to build reliable, high-performing AI systems. • Establish and uphold best practices for agent development, evaluation, and production observability. • Drive architecture and design decisions for agentic systems, owning tradeoffs across scalability, reliability, and risk across multiple teams. • Design and implement benchmarking and evaluation frameworks to measure and continuously improve AI system performance. • Translate Cboe's AI strategy into executable, prioritized technical work. • Lead technical design reviews and mentor engineers across the team, setting the standard for how AI systems are built at Cboe. • Produce clear technical documentation including scoping artifacts, design specs, and testing criteria.

The ideal candidate has

• Either: A Bachelor's or Master's degree in Computer Science, Engineering, or related field OR Equivalent demonstrated experience • 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 (language agnostic, Python strongly preferred) • 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

You'll really stand out if you have:

• 10-15+ years of overall professional experience, including 5+ years in applied AI • Experience in a consulting, platform, or internal services capacity and experience managing multiple workstreams • Prior experience owning an AI scoping or discovery practice, not just executing against pre-defined specs • Familiarity with AWS and/or Snowflake in a production context • Experience with NLP tasks such as summarization, classification, or entity extraction and their application in agentic contexts • Contributions to AI str

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