Staff Product Manager- AI Platform
New
Based in United StatesFull-TimeStaff
Salary$140,000–$208,000, plus bonus potential.
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Job Details
- Experience
- 8+ years
- Required Skills
- Product ManagementStakeholder managementLLM
Requirements
- 8+ years of product management experience, including significant experience building platforms, infrastructure products, or developer-facing solutions.
- Strong technical foundation through engineering, computer science, or equivalent hands-on experience with complex software systems.
- Deep understanding of AI-powered systems, distributed architectures, LLM-based agents, retrieval workflows, multi-step automation, and tool-based interactions.
- Experience defining and scaling platform contracts, APIs, configuration models, and lifecycle management strategies.
- Proven ability to introduce governance, standards, and quality frameworks while maintaining product velocity.
- Strong knowledge of AI system evaluation, observability, monitoring, and regression testing practices.
- Experience working in enterprise or multi-tenant environments where security, privacy, compliance, and reliability are critical.
- Strong developer empathy with experience improving adoption through tooling, documentation, and streamlined workflows.
- Demonstrated ability to make complex tradeoffs involving latency, cost, quality, scalability, and reliability.
- Exceptional communication and stakeholder management skills with the ability to align engineering, product, data, and business teams.
Responsibilities
- Own the product vision, strategy, and roadmap for an AI platform focused on reliability, safety, scalability, and measurable business outcomes.
- Define how AI agents operate in production, including orchestration patterns, context management, tool usage, workflows, and execution models.
- Establish scalable platform standards, including APIs, configuration frameworks, versioning strategies, and lifecycle management practices.
- Partner with engineering, data, security, and product teams to make decisions around architecture, infrastructure, performance, and operational complexity.
- Create adoption strategies through developer tools, shared components, documentation, and standardized workflows that enable teams to build efficiently.
- Establish quality, governance, and safety frameworks including evaluation processes, monitoring, access controls, and auditability.
- Shape the developer and product experience for building, testing, debugging, and managing AI-powered solutions.
- Evaluate build-versus-buy decisions and guide external technology integrations through well-defined platform approaches.
- Define success metrics and measure platform impact through adoption, performance, quality, reuse, and cost efficiency.
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