Senior AI Engineer, Release Engineering

Intercontinental Exchange, Inc. is building an internal, AI-powered agentic developer platform within the Release Engineering Development team, and this role focuses on designing, operating, and improving that platform end to end. You will own the agent platform and its AIDLC (AI Development Life Cycle) workflows, ensuring security, evaluation rigor, reliability in production, and effective adoption across engineering. What you’ll build and operate Design, build, and maintain the platform, including theagent API , MCP integrations, andintent-routing logic .Create MCP integrations that connect agents toCI/CD , source control , ticketing, anddeploymenttools, with clear schemas, robust error handling and logging, and defined scope boundaries. Build and maintain askill and agent registrythat maps user intent to the right agent and runtime context. Keep amodular architectureso new agents, skills, and MCP integrations can be introduced without disrupting existing capabilities. Design multi-step, tool-using agents that select tools, reason over context, and producereliable structured output .Author, test, and iterate onprompts, skill definitions, and context structures, while managingcontext windowsandtoken budgets .Plan for failure by anticipating agent failure, context degradation, and prompt drift, and by defining recovery behavior. Build release and CI/CD agents forpipeline triage , release readiness checks , change documentation, androllback assistance, in partnership with release and development teams. Curate and maintain theAIDLC, outlining best practices and championing the process across the company. Security, reliability, and governance Enforceleast-privilege , zero trustaccess for agentic automation using clearly defined identities (user or service account) and per-tool permissions. Implementhuman-in-the-loop approval workflowsfor high-risk actions such as deployments, promotions, and rollbacks. Protect againstprompt injection , data leakage, andoverprivileged agents .Ensureaudit logging , compliance, anddata-handling controlsfor agent actions and model inputs. Own platform reliability withhealth checks , runbooks, explicit failure modes, and support for release tooling, CI/CD pipelines, and AI workflows in production. Instrument agent runs withtracingandstructured loggingso incorrect decisions can be diagnosed after the fact. Evaluation, model operations, and observability Build and operate evaluation frameworks, including evaluation sets, prompt regression tests, and quality metrics for agents and skills. Evaluate and select models while managing routing, caching, token budgets, and cost attribution. Adoption and collaboration Contribute to developer-facing interfaces, including chat integrations within existing communication tools and a skills catalog. Drive adoption by mentoring teams, documenting reusable patterns, and gathering feedback from skeptical users and early adopters. Translate workflow needs from Engineering, Infrastructure, and release teams into effective agents and skills. Track emerging models and agentic use cases, and prototype technologies that matter. Write clean, tested, secure code, participate in code and design reviews, and document agent designs, prompt strategies, and platform decisions.