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Principal Product Manager, AI Platform

Angi
CompanyAngi
CategoryData & Analytics
LocationDenver
RemoteRemote
EmploymentNot stated
LevelManager
SalaryNot stated by the employer
Posted4 Aug 2026
Last verified8 Aug 2026
SourceEmployer ATS (ashby)
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Description
For over 30 years, Angi has powered the future of the home services industry, creating an environment where homeowners and pros benefit from more jobs done well. For homeowners, our platform is a reliable way to find skilled pros. For pros, we're a reliable business partner who helps them find the winnable work they want, when they want. For employees, we're an amazing place to call home. We can't wait to welcome you. Angi at a glance: - Founded in 1995 as Angie’s List and rebranded in 2021 - Global company with 9 brands in 8 countries and employees worldwide - Homeowners have turned to us for 300 million home projects and counting ABOUT THE TEAM 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. WHAT YOU'LL DO Product Management - 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 WHO YOU ARE Minimum Qualifications - 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 custom