Senior Product Manager
$250,000–$325,000 year
HybridBellevue, Washington, United States
Job Summary
Own the product vision, roadmap, and success metrics for the AI agent orchestration and governance layer and the enterprise semantic data layer. Translate ambiguous enterprise customer problems into actionable requirements, PRDs, and acceptance criteria to drive engineering execution. Lead the full product lifecycle from discovery through GA, including customer research, specification, build oversight, QA alignment, and post-release iteration. Serve as the primary interface to engineering leads, resolving scope ambiguities and unblocking delivery while maintaining quality. Deeply engage with AI/ML architecture, API contracts, and data pipelines to identify risks invisible to non-technical PMs. Drive compliance and regulatory readiness into product design, mapping capabilities to EU AI Act, NIST AI RMF, and SOC 2 requirements. Represent AIVista in client-facing discussions and architecture reviews with enterprise technical buyers and C-suite stakeholders.
Required Qualifications
- 7+ years of product management experience shipping AI/ML platform products, developer infrastructure, or enterprise data products at a product-focused technology company
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent hands-on technical experience
- Demonstrated ability to define product strategy and drive execution for technically complex, multi-workstream initiatives — from ambiguous problem definition through production release
- Deep technical fluency in AI/ML systems: sufficient depth to understand LLM orchestration patterns, RAG pipeline architecture, semantic data modeling, and agent runtime design, and to challenge technical assumptions independently
- Track record of writing high-quality product specifications (PRDs, functional specs, API contracts) that engineering teams can execute without constant clarification
- Experience driving alignment across engineering, data science, legal/compliance, and enterprise client organizations without direct authority
- Strong written communication: capable of producing product strategy memos, capability narratives, and customer-facing documentation that are clear, concise, and credible to both technical and non-technical audiences
Desired Qualifications
- MBA or Master's degree in Computer Science, Systems Engineering, or a related field
- Hands-on experience with agentic AI infrastructure: LLM orchestration frameworks (LangGraph, LangChain, AutoGen), RAG pipeline design, model governance, or intelligent document processing in production environments
- Familiarity with enterprise AI regulatory frameworks: EU AI Act, NAIC Model AI Bulletin, NIST AI RMF, SOC 2, or ISO/IEC 42001, and experience building compliance requirements into product design rather than retrofitting them
- Track record of translating complex technical capabilities into products that feel simple and inevitable to enterprise buyers, packaging sophisticated AI infrastructure into use cases that drive adoption and business outcomes
- Experience with Spec-Driven Development (SDD), OpenTelemetry-based observability, or model governance frameworks in production AI systems
- Client-facing product experience at enterprise scale (Fortune 500 or equivalent), with comfort owning product conversations at the CTO or CIO level
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