Staff / Senior Staff Engineer, AI Agent Engineering

Equinix•Toronto, ON

9d•CA$131,000 - CA$181,000

About The Position

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.

Requirements

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

Nice To Haves

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

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 cloud-native design