{"id":3815471,"url":"https://alion.io/job/intercontinental-exchange-senior-ai-engineer-release-engineering","title":"Senior AI Engineer, Release Engineering","company":{"id":58612,"name":"Intercontinental Exchange","domain":"ice.com","url":"https://alion.io/company/intercontinental-exchange","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":{"grade":"A","score":100,"open_postings":6,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":36,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Angular","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"Bitbucket","optional":false},{"name":"Chef","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Configuration Management","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"Go","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Least Privilege","optional":false},{"name":"Linux","optional":false},{"name":"LiteLLM","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Node JS","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"OpenShift","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"React.js","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TypeScript","optional":false},{"name":"Vertex AI","optional":false},{"name":"Zero Trust","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-11-05T15:43:00Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T09:27:18Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"At ICE, we are driven by a shared mission and a commitment to continuous progress. Our team comprises dedicated problem-solvers who begin with our customers’ needs, address their challenges, and turn them into valuable opportunities. With a start-up mindset, we remain agile, adaptable, and ready to act quickly.\nThe Senior Engineer is a full-stack engineer with strong AI and large language model (LLM) expertise, embedded within the Release Engineering Development team. This team designs and maintains processes that improve infrastructure delivery through application development, data management, and automation.\nIn this role, you will build and continuously evolve an internal, AI-powered developer platform. Central to this work is a genuine drive to advance the application of agentic AI frameworks by designing reliable agents, prompts, MCP integrations, and building our AIDLC (AI Development Life Cycle) while staying ahead of emerging LLM capabilities.\nYou will build and operate the agentic AI platform that is the core of our AIDLC which automates development, release, and operational workflows across engineering. This includes agents, skills, and MCP integrations that act across CI/CD, source control, ticketing, and deployment systems, with the security, evaluation, and auditability required for privileged access.\nResponsibilities\nAgent PlatformDesign, build, and maintain the platform - including the agent API, MCP integrations, and intent-routing logic.\nBuild MCP integrations that connect agents to CI/CD, source control, ticketing, and deployment tools, with clear schemas, robust error handling and logging, and defined scope boundaries.\nBuild and maintain the skill and agent registry that matches user intent with the appropriate agent and context at runtime.\nMaintain a modular architecture so new agents, skills, and MCP integrations can be added without disrupting existing capabilities.\n\nAI Development Life Cycle (AIDLC) - Agentic WorkflowsDesign multi-step, tool-using agents that select tools, reason across context, and produce reliable, structured output.\nAuthor, test, and iterate on prompts, skill definitions, and context structures while managing context windows and token budgets.\nDesign for failure by anticipating how agents fail, context degrades, and prompts drift - and by defining how the system recovers.\nBuild release and CI/CD agents for pipeline triage, release-readiness checks, change documentation, and rollback assistance in partnership with release and development teams.\nCurate and maintain the AIDLC, outlining best practices, and championing the process across the company.\n\nSecurity, Governance, and Human OversightEnforce least-privilege, zero trust, access for agentic automation, with clearly defined identities (user or service account) and per-tool permissions.\nDesign human-in-the-loop approval workflows for high-risk actions such as deployments, promotions, and rollbacks.\nProtect against prompt injection, data leakage, and overprivileged agents.\nEnsure audit logging, compliance, and data-handling controls for all agent actions and model inputs.\n\nEvaluation, Reliability, and CostBuild and operate evaluation frameworks, including evaluation sets, prompt regression tests, and quality metrics for agents and skills.\nEvaluate and select models while managing routing, caching, token budgets, and cost attribution.\nInstrument agent runs with tracing and structured logging so incorrect decisions can be diagnosed after the fact.\nOwn platform reliability through health checks, runbooks, and explicit failure modes - support release tooling, CI/CD pipelines, and AI workflows in production.\n\nAdoption and CollaborationContribute to developer-facing interfaces, including chat integrations within existing communication tools and the skills catalog.\nDrive adoption by mentoring teams, documenting reusable patterns, and gathering feedback from both skeptical users and early adopters.\nTranslate workflow needs from Engineering, Infrastructure, and release teams into effective agents and skills.\nTrack emerging models, agentic use cases, and prototype the technologies that matter.\nWrite clean, tested, and secure code. Participate in code and design reviews. Document agent designs, prompt strategies, and platform decisions.\n\nKnowledge and Experience\nEngineeringBachelor’s degree or an equivalent combination of education and experience.\n5+ years of professional experience in software engineering, DevOps, or release engineering, including platform or internal tooling work.\nHands-on experience with CI/CD and Git-based source control platforms such as GitHub or Bitbucket (GitHub preferred).\n\nAI, Agents, and LLMsUnderstanding of agentic patterns, including planning, tool selection, multi-step tasks, agent-loop design, and failure modes.\nStrong prompt-engineering and context-window management skills, including experience with skill definitions or scoped system prompts.\nWorking knowledge of AI security risks, including prompt injection.\nDaily use of AI coding tools such as Claude Code, GitHub Copilot, Codex, Cursor, or similar products.\n\nPreferred Knowledge and Experience\nExperience with agentic technologies such as LangChain, LlamaIndex, LiteLLM, or AutoGen, along with the judgment to determine when a leaner custom approach is more appropriate.\nExperience in Node.js/TypeScript, Go, and/or Python\nDemonstrated experience building and delivering at least one production or near-production agentic automation/process that uses LLM APIs, such as Anthropic, OpenAI, and LiteLLM, with tool calling and structured output.\nPractical experience with MCP or equivalent tool-calling frameworks, including schema design, agent-tool integration, and scope management.\nExperience evaluating LLM outputs by using evaluation sets, regression testing, and quality metrics.\nExperience with retrieval-augmented generation (RAG) and vector databases.\nExperience with cloud AI platforms such as Amazon Bedrock, Azure OpenAI, or Vertex AI.\nExperience with internal developer platforms or platform-as-a-product models.\nFrontend development experience with React or Angular.\nRelational database design experience.\nExperience with LLM observability tools.\nExperience with infrastructure as code and configuration management tools such as Terraform, Ansible, Puppet, or Chef.\nExperience working in regulated or audited environments.\nExperience with Agile practices and automated testing.\nExperience with containers and orchestration platforms such as Kubernetes or OpenShift, along with proficiency in Linux environments.\nExperience troubleshooting distributed systems (e.g., Kubernetes workloads, CI/CD pipelines, message queues).\nWhat We Look For\nAn engineer who treats agentic AI as a design discipline and has a demonstrated record of applying emerging LLM and agent capabilities in practice.\nA platform thinker who views agents, skills, MCP integrations, and the registry as one cohesive developer product.\nSomeone who takes ownership of reliability, security, and quality, and makes failure modes explicit.\nA collaborative partner who reduces friction across the broader engineering organization and can build trust with skeptical teams.\nIntercontinental Exchange, Inc. is an Equal Opportunity Employer. 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