Senior AI Platform Engineer

Cboe; Chicago (IL); On-site; USD 140,000 - 180,000; Full time

Job summary

Cboe seeks a Sr. Automation Engineer to architect and govern enterprise-scale intelligent automation powered by agentic AI. You will operate across AI engineering, security, and strategy to drive adoption of autonomous frameworks in complex, multi-team environments.

The role requires hands-on building and deploying production-grade AI systems with strong security, observability, and compliance controls, while translating technical trade-offs to executive leadership.

Qualifications

• Solid software engineering foundation (Python + compiled language) with experience in microservices, CI/CD, and infrastructure-as-code.
• Ability to design resilient AI systems with strong observability, tracing, and understanding of non-deterministic failure modes.
• Strong security mindset with expertise in AI threat modeling, prompt injection defense, zero-trust principles, and secure handling of model inputs/outputs.

Responsibilities

• Design, build, and govern enterprise-scale intelligent automation systems centered on agentic AI.
• Operate at the intersection of advanced AI engineering, security architecture, and organizational strategy.
• Translate emerging AI capabilities into production-grade systems that meet security and regulatory standards.
• Lead without authority across engineering, security, product, and operations in cross-functional environments.

Skills

Python

Compiled languages

Microservices

CI/CD

Infrastructure as Code

Observability

LLM APIs

RAG

Prompt engineering

Multi-agent architectures

Agentic AI systems

Security & threat modeling

Zero-trust

Red-teaming / penetration testing

Executive communication

AI governance

Financial services experience

Job description

• The Sr. Automation Engineer serves as a senior technical authority responsible for designing, building, and governing enterprise-scale intelligent automation systems centered on agentic artificial intelligence
• This role sits at the intersection of advanced AI engineering, security architecture, and organizational strategy — driving the adoption of autonomous agent frameworks across complex, multi-team environments
• The position demands a practitioner who can operate at both the deep technical layer and the executive advisory level, translating emerging AI capabilities into resilient, production-grade systems that meet the most rigorous security and compliance standards

Solid software engineering foundation (Python + compiled language) with experience in microservices, CI/CD, and infrastructure-as-codeAbility to design resilient AI systems with strong observability, tracing, and understanding of non-deterministic failure modesCapable of translating complex technical concepts into clear roadmaps, architecture decisions, and executive-level communicationStrong security mindset with expertise in AI threat modeling, prompt injection defense, zero-trust principles, and secure handling of model inputs/outputsDeep hands-on experience building and deploying agentic AI systems, including LLM APIs, RAG, prompt engineering, and multi-agent architecturesProven ability to operate cross-functionally and lead without authority across engineering, security, product, and operations5+ years of relevant experience across AI/ML systems, software engineering, or cybersecurity, with hands-on experience building, securing, and operating production-grade systemsProven track record building production-grade agentic AI systems that operate reliably at scaleHands-on experience designing agent orchestration patterns, debugging non-deterministic failures, and delivering observable, auditable AI pipelines in regulated environmentsStrong applied security expertise, including implementing defenses against prompt injection, jailbreaking, and tool misuse, with experience in red-teaming or penetration testing AI systemsDemonstrated ability to drive alignment across security, legal, compliance, product and engineering, turning competing priorities into practical, well-documented outcomesComfortable presenting architectural trade-offs and risk decisions to senior leadership in high-stakes environmentsExperience in financial services or regulated industries, with exposure to AI governance, model risk management, or internal AI policy development preferred