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Closed listing

Intercontinental Exchange Holdings, Inc.

Senior AI Engineer, Release Engineering

Atlanta, GA, United States
Jacksonville, FL, United States
Omaha, NE, United States

This opening has closed.

The original listing is kept below for reference.

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Job details

Experience: 5+ years of professional experience in software engineering, DevOps, or release engineering, including platform or internal tooling work.
Posting language: English
Posted: Nov 5, 2026

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Original job description

Archived listing. The details below describe a past opening.

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.

The 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.

In 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.

You 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.

Responsibilities

Agent Platform
Design, build, and maintain the platform - including the agent API, MCP integrations, and intent-routing logic.
Build 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.
Build and maintain the skill and agent registry that matches user intent with the appropriate agent and context at runtime.
Maintain a modular architecture so new agents, skills, and MCP integrations can be added without disrupting existing capabilities.
AI Development Life Cycle (AIDLC) - Agentic Workflows
Design multi-step, tool-using agents that select tools, reason across context, and produce reliable, structured output.
Author, test, and iterate on prompts, skill definitions, and context structures while managing context windows and token budgets.
Design for failure by anticipating how agents fail, context degrades, and prompts drift - and by defining how the system recovers.
Build release and CI/CD agents for pipeline triage, release-readiness checks, change documentation, and rollback assistance in partnership with release and development teams.
Curate and maintain the AIDLC, outlining best practices, and championing the process across the company.
Security, Governance, and Human Oversight
Enforce least-privilege, zero trust, access for agentic automation, with clearly defined identities (user or service account) and per-tool permissions.
Design human-in-the-loop approval workflows for high-risk actions such as deployments, promotions, and rollbacks.
Protect against prompt injection, data leakage, and overprivileged agents.
Ensure audit logging, compliance, and data-handling controls for all agent actions and model inputs.
Evaluation, Reliability, and Cost
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.
Instrument agent runs with tracing and structured logging so incorrect decisions can be diagnosed after the fact.
Own platform reliability through health checks, runbooks, and explicit failure modes - support release tooling, CI/CD pipelines, and AI workflows in production.
Adoption and Collaboration
Contribute to developer-facing interfaces, including chat integrations within existing communication tools and the skills catalog.
Drive adoption by mentoring teams, documenting reusable patterns, and gathering feedback from both skeptical users and early adopters.
Translate workflow needs from Engineering, Infrastructure, and release teams into effective agents and skills.
Track emerging models, agentic use cases, and prototype the technologies that matter.
Write clean, tested, and secure code. Participate in code and design reviews. Document agent designs, prompt strategies, and platform decisions.

Knowledge and Experience

Engineering
Bachelor’s degree or an equivalent combination of education and experience.
5+ years of professional experience in software engineering, DevOps, or release engineering, including platform or internal tooling work.
Hands-on experience with CI/CD and Git-based source control platforms such as GitHub or Bitbucket (GitHub preferred).
AI, Agents, and LLMs
Understanding of agentic patterns, including planning, tool selection, multi-step tasks, agent-loop design, and failure modes.
Strong prompt-engineering and context-window management skills, including experience with skill definitions or scoped system prompts.
Working knowledge of AI security risks, including prompt injection.
Daily use of AI coding tools such as Claude Code, GitHub Copilot, Codex, Cursor, or similar products.

Preferred Knowledge and Experience

Experience with agentic technologies such as LangChain, LlamaIndex, LiteLLM, or AutoGen, along with the judgment to determine when a leaner custom approach is more appropriate.
Experience in Node.js/TypeScript, Go, and/or Python
Demonstrated 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.
Practical experience with MCP or equivalent tool-calling frameworks, including schema design, agent-tool integration, and scope management.
Experience evaluating LLM outputs by using evaluation sets, regression testing, and quality metrics.
Experience with retrieval-augmented generation (RAG) and vector databases.
Experience with cloud AI platforms such as Amazon Bedrock, Azure OpenAI, or Vertex AI.
Experience with internal developer platforms or platform-as-a-product models.
Frontend development experience with React or Angular.
Relational database design experience.
Experience with LLM observability tools.
Experience with infrastructure as code and configuration management tools such as Terraform, Ansible, Puppet, or Chef.
Experience working in regulated or audited environments.
Experience with Agile practices and automated testing.
Experience with containers and orchestration platforms such as Kubernetes or OpenShift, along with proficiency in Linux environments.
Experience troubleshooting distributed systems (e.g., Kubernetes workloads, CI/CD pipelines, message queues).

What We Look For

An engineer who treats agentic AI as a design discipline and has a demonstrated record of applying emerging LLM and agent capabilities in practice.
A platform thinker who views agents, skills, MCP integrations, and the registry as one cohesive developer product.
Someone who takes ownership of reliability, security, and quality, and makes failure modes explicit.
A collaborative partner who reduces friction across the broader engineering organization and can build trust with skeptical teams.

Intercontinental Exchange, Inc. is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to legally protected characteristics.

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Skills

ICE
CI/CD
Documentation
Regulatory compliance
Communication skills
Coaching
Workflow
DevOps
Git
GitHub
Atlassian Bitbucket
Security threats
LangChain
Node.js
TypeScript
Go
Python
Microsoft Azure
Front-end development
React
Infrastructure as code
IBM Terraform
Ansible
Puppet
Agile
Test automation
Kubernetes
Red Hat OpenShift
Linux
Troubleshooting

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