Staff / Senior Staff Engineer (AI Agent Engineering)

Join Equinix, a global leader in digital infrastructure, as a Staff/Senior Staff Engineer in AI Agent Engineering. In this role, you will design, build, and ship LLM-powered agents that execute real lifecycle work, while also engineering the scaffolding that makes agents dependable. You will contribute to the orchestrator, persona consoles, and dashboards that keep humans in command of agent-led delivery. This is an opportunity to change how an entire organization builds software, with the autonomy of a startup and the reach of a global platform company.

• Design, build, and ship LLM-powered agents that execute real lifecycle work, including intake triage, estimation, requirements, technical design, coding, testing, release, and operations.
• Engineer the scaffolding that makes agents dependable, including tool use via MCP, agent-to-agent handoffs (A2A), event-driven orchestration, and deep Jira and enterprise system integration.
• Define guardrails, human-in-the-loop approval points, review thresholds, and escalation paths, so agent autonomy is earned, not assumed.
• Sound judgment about when to trust automation and when to demand human review, and the communication skills to explain that reasoning
• Experience designing evaluations for AI systems, or strong test-engineering instincts you are eager to apply to non-deterministic software
• 6+ years (Staff) or 9+ years (Senior Staff) of professional software engineering experience, with a record of shipping and operating production systems
• Fluency with modern engineering practice: Git, automated testing, CI/CD, observability, and cloud platforms
• Hands-on experience building LLM-powered applications or agents: prompt and context engineering, tool calling, retrieval, or multi-agent workflows
• Strong proficiency in Python or TypeScript, plus solid API, microservices, and event-driven architecture skills
• Experience with agent frameworks and protocols such as MCP, A2A, Anthropic or OpenAI APIs, Bedrock, Vertex, or LangGraph
• Experience building developer platforms, orchestration systems, or SDLC tooling, including Jira, GitHub, or ServiceNow integration
• Knowledge-engineering experience: retrieval systems, embeddings, or enterprise knowledge graphs
• Experience taking AI features through security, privacy, and responsible AI review in an enterprise
• Evidence of craft: open-source contributions, technical writing, or internal platforms with devoted users