Staff / Senior Staff Engineer, Equinix

• 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
• Strong proficiency in 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
• LLM application architecture; prompt and context engineering; tool use and orchestration; multi-agent design
• Eval design and automation; guardrails and human-in-the-loop gates; AI observability; responsible AI governance
• API and event-driven design; CI/CD and automation; cloud-native engineering; enterprise integration
• Systems thinking; pragmatic risk-taking; mentoring and standards-setting; clear communication

Desirable

• 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
• 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 Equinix's enterprise AI platform: AI gateway, orchestration, audit, and access control, with security and privacy by design
• Make Agents Trustworthy: Evals, Guardrails, Gates
• Design and automate eval suites that measure agent output quality on every change, and make 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 knowledge layers agents depend on: 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 engineering craft. 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
• 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