Principal Product Manager, Agent Intelligence

Equinix

. Define quality standards for agents and end-to-end systems, including correctness, grounding, completeness, tool-use accuracy, workflow adherence, latency, cost and business outcomes .

Posted 11/4/2026full-timeDallas • Texas • United StatesLead💰 $158,000 - $238,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and implementing quality standards for ML and LLM products, with a strong focus on evaluation systems, data governance, and cross-functional leadership. Proficient in hands-on data skills and capable of translating product requirements into measurable evaluations.

Highest-signal resume keywords

Product Management ExperienceEvaluation Systems DevelopmentHands-On Data SkillsCross-Functional LeadershipExperience with LLM Evaluation Tools

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonEvaluation StandardsQuality GatesData GovernanceExperimentation PlatformsCausal MeasurementReliability MeasurementMulti-Component SystemsObservability Systems

Soft Skills

Strong Evaluation JudgmentAbility to Explain Trade-OffsInfluencing Without Authority

Tools & Technologies

OpenTelemetryMCPA2A

Industry Keywords

ML ProductsLLM ProductsDistributed SystemsData PlatformsInfrastructure DataRegulated Customers

Tech Stack

Tools & technologies

Distributed SystemsPythonSQL

About the role

Key responsibilities & impact

Define quality standards for agents and end-to-end systems, including correctness, grounding, completeness, tool-use accuracy, workflow adherence, latency, cost and business outcomes
Convert product requirements and acceptance criteria into measurable evaluations with thresholds and owners
Establish scoring standards and rubrics across agents, workflows, regions and industries
Own the roadmap for offline and online evaluation, golden datasets, scenario and regression suites, LLM-as-judge pipelines and human-in-the-loop review
Implement evaluation-based quality gates in CI/CD for model, prompt, tool and data changes
Measure reliability using repeated trials, consistency, confidence intervals and confidence calibration
Evaluate retrieval, entity resolution, ranking, tool and MCP calls, agent handoffs and final outputs
Build trajectory- and trace-level evaluation to identify failure-causing components and error propagation
Define component contracts covering schemas, provenance and confidence
Partner with engineering on tracing and telemetry across agents, tools and data sources
Monitor drift in inputs, data sources, models and tool behavior and alert on regressions
Provide leaders and product teams with views of quality, reliability, cost and adoption
Analyze production traces, user edits, dismissals, ratings and expert audits for failure modes and data gaps
Convert confirmed failures into regression tests, product requirements or training and tuning data
Run experiments and A/B tests measuring accuracy, reliability, adoption and business outcomes
Enforce data boundaries and confidentiality controls in partnership with security, legal and data governance
Set evaluation standards adopted by other teams and influence teams without direct authority
Write strategy, specifications and executive briefs
In the first year, stand up a shared evaluation platform and golden datasets, add eval gates to every agent release, ship end-to-end tracing, and convert production failures into regression tests

Requirements

What you’ll need

7+ years across product management and engineering
3+ years as a product manager on technical platform, infrastructure, data or ML products
Several years building or operating distributed systems, data platforms, infrastructure or ML systems as an engineer, or equivalent hands-on depth
Track record of building and running evaluation, quality or observability systems for production ML or LLM products at scale
Direct experience with multi-component systems such as agents, tool use, retrieval pipelines or orchestrated services
Strong evaluation judgment, including code-based checks, LLM judges, human review and judge calibration
Hands-on data skills: SQL, traces and logs, and Python or similar for prototyping
Ability to lead across engineering, data science and business teams and explain trade-offs to senior leaders
Experience with LLM evaluation and observability tools, OpenTelemetry, 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
A degree in computer science, engineering or another quantitative field, or equivalent experience (nice to have)

Benefits

Comp & perks

Employee Assistance Program
US health, life, disability and voluntary insurance plans
US retirement plan contributions
Accrued paid time off and paid holidays
Canada healthcare coverage, life, disability and optional benefit plans
Canada Defined Contribution Pension Plan (DCPP), Group Retirement Savings Plan (RRSP) and Tax-Free Savings Plan (TSFA)
Canada vacation, personal time and paid holidays
Bonus eligibility
Equity may be offered depending on the position
Disability accommodations available