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 career at Equinix means being at the center of shaping what comes next and amplifying customer value through innovation and impact. You’ll work across teams, influence key decisions, and help shape the path forward.

Job Summary We’re looking for a Principal Product Manager to own Agent Intelligence: the evaluation, observability and feedback systems that tell us how well our agents perform, why they fail, and whether each change makes them better. You will set the quality bar for agents across Equinix and build the platform that enforces it. Now you want to apply that to agents working on real enterprise data, at global scale, where being wrong has real consequences.

Responsibilities Set the quality bar Own the roadmap for offline and online evaluation. This includes golden datasets, scenario and regression suites, LLM-as-judge pipelines calibrated against human experts, and human-in-the-loop review Make evaluation part of how agents ship, with eval-based quality gates in CI/CD. No model, prompt, tool or data change reaches users without passing agreed thresholds Build trajectory- and trace-level evaluation that traces an end-to-end failure back to the component that caused it, and test how errors spread across handoffs Define the contracts between components (schemas, provenance, confidence) so quality can be checked at every boundary Partner with engineering on tracing and telemetry across agents, tools and data sources, so every output can be traced to its inputs, sources and decisions Build monitoring for drift in inputs, data sources, models and tool behavior, with alerts that catch regressions before users do Give leaders and product teams a trusted view of quality, reliability, cost and adoption by agent, workflow and segment Mine production traces, user edits, dismissals, ratings and expert audits for recurring failure modes,



edge cases and data gaps Turn every confirmed failure into a regression test, a product requirement, or training and tuning data, and track it through to a fix Run experiments and A/B tests that show whether changes improve accuracy, reliability, adoption and business outcomes Build evals and controls that enforce data boundaries.

Internal data stays internal, customer-facing content uses only approved material, and confidential details never cross between accounts Partner with security, legal and data governance to meet regulatory and confidentiality requirements, including for customers in regulated industries Write the strategy, specs and executive briefs that align engineering, data science, sales and leadership Ship end-to-end tracing, with failures traced to the responsible component, across our multi-agent workflows Close the loop so production failures become regression tests within days, not quarters 7+ years across product management and engineering, including 3+ years as a product manager on technical platform, infrastructure, data or ML products ~ A systems engineering background: several years building or operating distributed systems, data platforms, infrastructure or ML systems as an engineer, or equivalent hands‑on depth ~ A track record of building and running evaluation, quality or observability systems for production ML or LLM products at scale (many teams, many models, or high request volumes), with results to show for it ~ Direct experience with multi-component systems, such as agents, tool use, retrieval pipelines or orchestrated services, where you had to find which component caused a failure ~ You know when to use code-based checks, LLM judges or human review,



how to calibrate a judge against experts, and why a metric can look good while quality is poor ~ Hands‑on data skills: you write SQL, read traces and logs, and can prototype an eval or analysis yourself in Python or similar ~ Ability to lead without authority across engineering, data science and business teams, and to explain trade‑offs clearly to senior leaders Experience with enterprise data such as CRM, billing, product usage, or network and infrastructure data Experience with regulated customers or data governance, such as financial services, privacy or confidentiality controls A degree in computer science, engineering or another quantitative field, or equivalent experience You will own the platform and the quality bar, and you will be accountable for our agents getting measurably better Agent quality is defined, measured and trusted, and product, engineering and leadership all use the same numbers Evals and monitoring catch failures before users report them, and each failure becomes a permanent regression test Why this role matters AI agents are becoming part of how Equinix understands its customers and acts on opportunities. As they take on more complex research, recommendations and actions across many connected components, the ability to measure them, explain their failures and improve them continuously becomes the foundation for trusting them. You will build that foundation.

United

States - Dallas Infomart Office DAI : 158,000 - 238,000 USD / Annual Canada - Toronto Office TRO : 158,000 - 236,000 CAD / Annual Our pay ranges reflect the minimum and maximum target for new hire pay for the full time position determined by role, level, and location. Individual pay is based on additional factors including job-related skills, experience, and relevant education and/or training. The targeted pay range listed reflects the base pay only and does not include bonus, equity, or benefits.

Employees are eligible for bonus, and equity may be offered depending on the position.

📌 Principal Product Manager, Agent Intelligence (Toronto)
🏢 Equinix
📍 Toronto