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Equinix
Equinix

Staff Agentic AI Engineer

Bangalore Office BLS2
3+ years exp
Full-time
Posted 5d ago
1 views
Actively Hiring Direct 1-Click Apply

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Job Description

Who are we?

Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.

A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.

Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

Job Summary

Build the Agents That Build Our Software

Most engineering roles now come with AI tools. This 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 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 cloudnative 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 Qualifications

  • 3+ years 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

Preferred Qualifications

  • 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

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-driven design; CI/CD and automation; cloud-native engineering; 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; 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 exists to do exactly that.

Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.

Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.

We use artificial intelligence in our hiring process. Learn more here.

This posting is a new position within our organization.

Key Requirements & Skills

  • 3+ years 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
  • 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

Benefits & Perks

medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.

Frequently Asked Questions

How to apply for Staff Agentic AI Engineer at Equinix?

Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.

What is the salary for this role?

Salary details will be discussed during the interview.

What experience is required?

3+ years of experience is required.

Is this position still open?

Yes, currently active and accepting applications.

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Equinix

Equinix

Equinix is the largest global data center and colocation provider for enterprise network and cloud computing. Secure datacenter management technologies and systems.

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Staff Agentic AI Engineer

Equinix · Bangalore Office BLS2

Apply on Company Website