Principal Product Manager, Agent Intelligence

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. 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. 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 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. This role is for someone who has done this before at scale. You have built or run evaluation or observability for production ML or LLM systems with many moving parts. You came up through systems engineering, and you can go from a dashboard to a trace to a root cause yourself. 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 Define what “good” means for each agent and for the system end to end. That covers correctness, grounding in sources, completeness, tool-use accuracy, workflow adherence, latency, cost and business outcome Turn product requirements and acceptance criteria into measurable evals, each with a clear threshold and owner Set scoring standards and rubrics that hold across agents, workflows, regions and industries, and that both experts and automated judges apply consistently Build the evaluation platform 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 Measure reliability, not just accuracy, using repeated trials, consistency across runs, confidence intervals and confidence calibration Evaluate multi-component systems Evaluate each component and the whole system: retrieval, entity resolution, ranking, tool and MCP calls, handoffs between agents, and final outputs 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 Make agent behavior observable 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 Turn real use into improvement 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 Protect trust 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 Lead across the organization Set evaluation standards that other product and engineering teams adopt, and influence teams you don’t manage Write the strategy, specs and executive briefs that align engineering, data science, sales and leadership In your first year, you will Stand up a shared evaluation platform and golden datasets for Equinix’s core agent workflows Put eval gates in front of every agent release 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 Qualifications Required 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 Strong evaluation judgment. 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 Nice to have Experience with LLM evaluation and observability tools, tracing standards such as OpenTelemetry, and agent protocols such as MCP and A2A Experience with retrieval, ranking, recommendation, confidence calibration or learning-from-feedback systems Experience with experimentation platforms and causal measurement 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 What this role is not This is not a prompt-engineering role, a reporting role, or a role managing a labeling vendor. You will own the platform and the quality bar, and you will be accountable for our agents getting measurably better What success looks like Agent quality is defined, measured and trusted, and product, engineering and leadership all use the same numbers No agent change reaches users without passing its eval gates When an output is wrong, the team can quickly see which component caused it Evals and monitoring catch failures before users report them, and each failure becomes a permanent regression test Equinix agents become measurably more accurate, reliable and widely adopted over tim 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. The targeted pay range for this position in the following location is / locations are: 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 determin