C
Cboe
Senior Engineer (Applied Artificial Intelligence)
Skills
PythonSnowflakeAIAWSWorkdayNLP / Natural Language Processing
2 months ago
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Important information
Contract type:
Permanent
Salary:
$140,250 – $181,500 / year
Location:
Chicago, IL, USA
Work mode:
Onsite
Published on:
7 August 2026
What they need
- 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
- 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- 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
- Experience with LLM evaluation, observability, and benchmarking as core engineering practice
- Hands-on experience building tools and integrations for LLM-based systems
- Strong programming skills (language agnostic, Python strongly preferred)
- Either: A Bachelor’s or Master’s degree in Computer Science, Engineering, or related field OR Equivalent demonstrated experience
- Strong written and verbal communication skills, particularly the ability to explain complex AI concepts to non-technical audiences
- Ability to work independently, manage ambiguity, and deliver results without close oversight
- 3+ years of professional experience in applied AI (building agents, MCP servers, context/harness engineering)
- Demonstrated ability to work directly with non-technical stakeholders to scope AI opportunities and define technical requirements
- 7–10+ years of relevant professional experience in software engineering, with increasing seniority
- Deep proficiency in context engineering including prompt design, retrieval strategies, and memory/context management
- Track record of setting technical standards, leading design/architecture reviews, and mentoring engineers. This role carries a formal technical leadership expectation
- 10-15+ years of overall professional experience, including 5+ years in applied AI
- Prior experience owning an AI scoping or discovery practice, not just executing against pre-defined specs
- Experience in a consulting, platform, or internal services capacity and experience managing multiple workstreams
- 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 strategy, roadmap definition, or architectural decision-making at an organizational level
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