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# Principal Product Manager, AI Platform

- **Company:** HomeAdvisor Powered By Angi
- **Location:** Denver, CO, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $190,000.0 - $280,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Management of Software Versions, Datadog, Large Language Models, Rate Limiting, AI Platforms, Kubernetes, Machine Learning Operations, Api Gateway
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ac4beebc09390329

## About the Role

* 8+ years of experience in Product Management, with at least 4 years focused on infrastructure, platforms, ML/AI systems, or other technical products serving internal engineering or data-science customers.
 * Proven track record scaling technical platforms from inception through maturity for demanding internal customers.
 * Technical fluency across the modern AI/ML stack - model serving and inference, API gateways/proxies, evaluation and testing frameworks, and cloud/Kubernetes infrastructure - sufficient to "swim with the fishes" with engineers and data scientists on architecture trade-offs.
 * Demonstrated ability to use telemetry and cost data (model performance, eval results, adoption analytics, spend data) to diagnose bottlenecks and drive product strategy., * Hands-on familiarity with LLM gateways/proxies (e.g., LiteLLM), evaluation and observability tooling (e.g., Langfuse), and model serving frameworks (e.g., KServe, SageMaker, Bedrock, Ray).
 * Experience with prompt management and versioning, LLM fine-tuning, or self-hosted/open-weight model serving in production.
 * Familiarity with agent governance concepts - guardrails, PII controls, prompt-injection protection, and autonomy/trust frameworks - and with the tension between agent velocity and cost control.
 * Experience operating a platform across competing tracks and stakeholders, sequencing revenue-critical stabilization work alongside a new strategic build-out.
 * Exceptional leadership skills with a history of influencing cross-functional teams without direct authority.

## Description

This Principal Technical Product Manager will own the vision, strategy, and roadmap for the AI Platform: the shared, governed entry point every model and agent workload at Angi runs through. As generative AI moves from experiment to production across the company, this team manages the common platform with routing, cost attribution, quality evaluation, and guardrails built in rather than rebuilt by every team.

The ideal candidate is a technical product manager who treats infrastructure as a product and has a strong thesis on how AI is reshaping the way software and models get built and served. You will own the platform that our product engineering, data science and their associated agents depend on: a model-agnostic AI Gateway, eval-gated prompt management, self-hosted and fine-tuned model serving, and the trust-and-autonomy ladder that governs how much independence each agent earns. Your measure of success is not just adoption and scale, but are we meaningfully improving our products and enabling our internal teams., * AI Gateway & Cost Governance: Own the vision for a governed, model-agnostic gateway that all model and agent traffic routes through, with per-team and per-use-case cost attribution, flexible model routing, rate limiting, provider failover, and automatic spending caps and stop switches. Make model-swap and cost decisions changeable once at the gateway, not per service.

 * Evaluation & Quality Enforcement: Define the roadmap for the eval engine and the pass/fail gate that runs on it, eval-gated prompt management with versioning and rollback, and per-agent accuracy scoring - so quality regressions are caught before they reach users rather than surfacing downstream in business metrics.
 * Trust & Autonomy Ladder: Define and champion the trust-and-autonomy ladder - the thresholds, progression criteria, evidence, approvals, and rollback logic that govern when an agent earns more independence - so agentic adoption scales with accountability instead of governance gaps.
 * LLM & ML Serving Infrastructure: Own the production path self-hosted LLM /open-weight model serving and traditional ML with the goal to consolidate both ML and LLM workloads under one standardized observable platform.
 * Cost & ROI Telemetry: Define and instrument the metrics that track the platform health as well as business impact to the platform.

Execution And Leadership

 * Cross-Functional Partnership: Establish deep partnerships with product engineering, architecture and data science to drive adoption and make sure the platform reflects how teams actually build and serve models.
 * Technical Roadmap Management: Manage a complex platform backlog spanning parallel tracks and under real capacity constraints. Make and communicate authoritative sequencing and trade-off decisions with concise and clear communications.
 * Stakeholder Communication: Act as the primary interface between technical platform teams and business stakeholders, translating infrastructure investment into clear business outcomes


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