AI Product Manager, AI Platform - Quality and Evaluation

Ridgeline•Reno, NV

2d•$136,000 - $187,000•Onsite

About The Position

As a Product Manager on Ridgeline's AI Platform team, you'll define and execute the strategy for the systems that ensure our AI capabilities are accurate, reliable, and continuously improving. You'll lead initiatives spanning LLM evaluation frameworks, AI model benchmarking, evaluation harnesses, prompt and model experimentation, AI quality platforms, and observability capabilities that help teams understand, measure, and improve production AI performance. You'll work closely with engineering, AI researchers, design, and product teams to deliver platform capabilities that enable every AI-powered experience across Ridgeline. You'll leverage cutting-edge AI technologies and tools in a fast-moving, creative, progressive work environment while helping establish best practices for developing and operating enterprise AI products. At Ridgeline, how we work matters as much as what we build. Ridgeliners act like owners, choose growth over comfort, and communicate with transparency. We assume positive intent, bias toward action, and bring solutions—not just problems. We celebrate wins, learn from setbacks, and thrive in a resilient, collaborative, high-performing culture. If this excites you, we’d love to meet you! You must be work authorized in the United States without the need for employer sponsorship.

Requirements

5+ years of product management experience building highly technical platform, infrastructure, or developer-focused products
Demonstrated experience shipping production AI products powered by large language models
Experience defining products that incorporate LLM evaluation frameworks, AI quality measurement, and continuous model evaluation
Deep understanding of offline and online evaluation methodologies, model benchmarking, regression testing, and AI quality metrics
Experience with LLM observability platforms such as Datadog LLM Observability, Langfuse, LangSmith, Arize AI, Braintrust, Galileo, or similar AI quality and evaluation solutions
Familiarity with prompt engineering, experimentation frameworks, evaluation datasets, and LLM-as-a-judge techniques for generative AI applications
Experience partnering effectively with software engineers, ML engineers, AI researchers, and cross-functional stakeholders
Strong analytical skills with the ability to translate AI performance signals into product decisions
Excellent written and verbal communication skills
Willingness to learn about cutting-edge technologies while cultivating expertise in investment management workflows and enterprise AI
An aptitude for problem solving
Ability to communicate effectively
Serious interest in having fun at work

Nice To Haves

Experience building products around retrieval-augmented generation (RAG), AI agents, MCP, or model orchestration frameworks
Experience with synthetic data generation and automated evaluation pipelines
Experience building internal developer platforms or ML infrastructure products
Experience in enterprise SaaS, developer platforms, or financial technology

Responsibilities

Define and execute the product strategy and roadmap for Ridgeline's AI Quality Platform
Build scalable LLM evaluation frameworks and automated evaluation harnesses that enable continuous model validation
Establish standardized offline and online evaluation methodologies, golden datasets, and quality benchmarks across AI products
Define the roadmap for AI observability, enabling teams to monitor quality, latency, cost, reliability, and user outcomes in production
Partner with engineering and AI researchers to build continuous regression testing and experimentation capabilities for generative AI systems
Drive product decisions using AI quality metrics, customer feedback, and quantitative evaluation data
Collaborate with engineering teams to deliver platform capabilities that enable trusted, measurable, and continuously improving AI experiences across Ridgeline
Translate advances in foundation models, evaluation techniques, and AI tooling into platform capabilities that create meaningful customer value
Prioritize product investments using customer insights, product analytics, and measurable business outcomes
Communicate product vision, roadmap, and priorities clearly across technical and executive stakeholders
Foster a collaborative environment rooted in ownership, continuous learning, experimentation, and transparency