Staff Senior Staff Engineer, AI Agent Engineering

Job Summary

Most engineering roles now come with AI tools. This one comes with a mission. Equinix Incubation builds the agentic systems that run the software delivery lifecycle end to end: from intake and business case through design code test release and value tracking with humans directing the work and owning every gate.

We are hiring Staff and Senior Staff Engineers to build those agents and the platform they run on. You will not just use AI to code faster. You will design ship evaluate and harden production agents that colleagues across a global organization trust with real delivery work. Your agents will write software; your engineering decides what ships.

Responsibilities

Build Production AI Agents

• Design build and ship LLM-powered agents that execute real lifecycle work: intake triage estimation requirements technical design coding testing release and operations

• Engineer the scaffolding that makes agents dependable: tool use via MCP agent-to-agent handoffs (A2A) event-driven orchestration and deep Jira and enterprise system integration

• Build on Equinixs enterprise AI platform: AI gateway orchestration audit and access control with security and privacy by design

Make Agents Trustworthy: Evals Guardrails Gates

• Design and automateevalsuites that measure agent output quality on everychange andmake passing evals the release gate for agents

• Define guardrails human-in-the-loop approval points review thresholds and escalation paths so agent autonomy is earned not assumed

• Instrument agent behavior end to end (quality latency cost adoption) find failure patterns and tune prompts context and configurations until the numbers move

Engineer Context and Knowledge

• Build the knowledgelayersagents dependon:retrieval over process libraries decision histories code and delivery data

• Establish reusable prompt patterns context standards and agent configurations that other teams adopt

• Own agents through their full lifecycle: instructions context freshness performance monitoring feedback and retirement

Ship the Platform and Raise the Bar

• Contribute to the orchestrator persona consoles and dashboards that keep humans in command of agent-led delivery

• Dogfood relentlessly: use agents to build agent systems and feed what you learn back into the platform

• Bring strong engineeringcraft. The fundamentals still decide whether this works: architecture code quality testing CI/CD and cloud-native design

What Success Looks Like

• Agents you built are doing live delivery work with measurable cycle-time and quality gains and humans confidently in control

• Your eval suites are the reason people trust agent output; passes evals means something because you made it mean something

• Your context patterns guardrails and agent standards are reused by teams you have never met

• You can explain to an executive in plain language what an agent did why and how you know

• The platform gets simpler faster and cheaper as it scales because you treat agent cost and reliability as engineering problems

Level Expectations

• Staff:You deliver complete agents and platform components within established patterns own their evals and quality end to end and are the dependable engine of your pod

• Senior Staff:You set the patterns. You take the hardest most ambiguous problems (orchestration eval design agent reliability at scale) define the standards others follow and multiply the team

Qualifications
Required

• 6 years (Staff) or 9 years (Senior Staff) of professional software engineering experience with a record of shipping and operating production systems

• Hands-on experience building LLM-powered applications or agents: prompt and context engineering tool calling retrieval or multi-agent workflows

• Experience designing evaluations for AI systems or strong test-engineering instincts you are eager to apply to non-deterministic software

• Strongproficiencyin Python or TypeScript plus solid API microservices and event-driven architecture skills

• Fluency with modern engineering practice: Git automated testing CI/CD observability and cloud platforms

• Sound judgment about when to trust automation and when to demand human review and the communication skills to explain that reasoning

Preferred Qualifications

• Experience with agent frameworks and protocols such as MCP A2A Anthropic or OpenAI APIs Bedrock Vertex orLangGraph

• 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

Core Competencies

Agent Engineering

• LLM application architecture; prompt and context engineering; tool use and orchestration; multi-agent design

Evals and Trust

• Eval design and automation; guardrails and human-in-the-loop gates; AI observability; responsible AI governance

Platform Craft

• API and event-drivendesign;CI/CD andautomation;cloud-nativeengineering;enterprise integration

Judgment and Impact

• Systems thinking; pragmatic risk-taking; mentoring and standards-setting; clear communication

Why This Role

Incubation is a durable capability not a project team: the team persists and the product rotates. Agentic delivery is product one; the next incubation bets follow. You will help define how AI-first engineering works at Equinix with the autonomy of a startup and the reach of a global platform company. Few roles let you change how an entire organization builds software. This one exists to do exactly that.