Intercontinental Exchange, Inc. is building an internal, AI-powered agentic developer platform embedded in the Release Engineering Development team. In this role, you will own core platform capabilities end to end, focusing on security, evaluation, reliability, and real adoption with release and development partners.
Location: Atlanta, GA (onsite)
Experience: 5+ years
Education: Bachelor’s degree or equivalent combination of education and experience
What you’ll do
- Design, build, and maintain the agent platform, including the agent API, MCP integrations, and intent-routing logic.
- Develop 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 to the appropriate agent and runtime context.
- Maintain a modular architecture so new agents, skills, and MCP integrations can be added without disrupting existing capabilities.
- Create 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, including context window and token budget management.
- Design for failure by anticipating agent failure, context degradation, and prompt drift, and defining system recovery behaviors.
- 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.
- Enforce least-privilege, zero trust access for agentic automation using clearly defined identities (user or service account) and per-tool permissions.
- Implement human-in-the-loop approval workflows for high-risk actions such as deployments, promotions, and rollbacks.
- Protect the system against prompt injection, data leakage, and overprivileged agents.
- Ensure audit logging, compliance, and data-handling controls for all agent actions and model inputs.
- Build and operate evaluation frameworks, including evaluation sets, prompt regression tests, and quality metrics for agents and skills.
- Evaluate and select models with routing, caching, token budgets, and cost attribution.
- Instrument agent runs with tracing and structured logging to support post-incident diagnosis.
- Own platform reliability via health checks, runbooks, and explicit failure modes supporting production release tooling, CI/CD pipelines, and AI workflows.
- Contribute to developer-facing interfaces, including chat integrations within existing communication tools and a skills catalog.
- Drive adoption through mentoring, documenting reusable patterns, and collecting 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, and secure code, and participate in design and code reviews while documenting agent designs, prompt strategies, and platform decisions.
Requirements
- 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).
- 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.
Technologies you may use
CI/CD, GitHub, Bitbucket, Claude Code, GitHub Copilot, Codex, Cursor, LangChain, LlamaIndex, LiteLLM, AutoGen, Node.js, TypeScript, Go, Python, Anthropic, OpenAI, MCP, RAG, vector databases, Amazon Bedrock, Azure OpenAI, Vertex AI, React, Angular, Terraform, Ansible, Puppet, Chef, Kubernetes, OpenShift, Linux, Git, Kubernetes workloads, message queues
Preferred knowledge and experience
- Experience with agentic technologies such as LangChain, LlamaIndex, LiteLLM, or AutoGen, with 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 or process that uses LLM APIs (Anthropic, OpenAI, and LiteLLM), including 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 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, with proficiency in Linux environments.
- Experience troubleshooting distributed systems (for example: Kubernetes workloads, CI/CD pipelines, message queues).
What we look for
- An engineer who treats agentic AI as a design discipline, with a 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, with explicit failure modes.
- A collaborative partner who reduces friction across the broader engineering organization and can build trust with skeptical teams.
