Staff Product Manager, Agentic AI Applications

Responsibilities

• Own the Agentic Platform strategy and roadmap. Define what ships, in what order, and why. Translate organizational outcomes into concrete platform capabilities with measurable success criteria.
• Define and drive the agent runtime. Establish the managed agent runtime supporting multi-step orchestration with durable execution, model gateway abstraction across all providers, governed tool invocation, and configurable per-agent guardrails (cost ceilings, timeouts, blast radius limits).
• Build the MCP connector ecosystem. Own the strategy for standardized, bidirectional connectors to various systems of record. Drive identity propagation, idempotency, dry-run/preview mode, and a connector SDK that lets domain teams onboard new systems without platform changes.
• Establish the intelligence layer. Define the three-layer data architecture: knowledge graph (curated domain knowledge), context graph (live entity state from systems of record), and temporal memory (session, user preferences, and episodic history). Ensure unified retrieval across vector, structured, and graph sources with source traceability on every context element.
• Ship the evaluation and quality framework. Own the AI-judge evaluation pipeline: offline eval with golden datasets, online LLM-as-judge scoring, domain-specific judges (Finance, HR, Legal, Sales), and mandatory evaluation gates in CI/CD.
• Design the developer experience. Make the platform self-service by construction. Domain teams provision agent projects, promote across environments, and access connectors without platform-team tickets. SDK, CLI, sandbox environments, agent templates, and documentation.
• Define the federation and adoption model. Establish the three tiers of adoption (platform built, domain built on platform, citizen developer edge apps) with governance checkpoints at each gate. Drive the gold-standard promotion pipeline from edge prototype to production hardened service.

Requirements

• 8+ years of product management experience, with at least 3 years on internal platform, infrastructure, or developer-experience products.
• Deep experience building platforms that other teams build on; understand the difference between a platform and an application, and have opinions about API design, developer ergonomics, and self-service.
• Demonstrated experience with AI/ML platforms, agent frameworks, LLM-powered applications, or agentic systems.
• Strong technical foundation; can read architecture diagrams, discuss trade-offs with engineers (e.g., sync vs. async, checkpointing strategies, context window management), and make informed prioritization decisions on deeply technical work.
• Experience defining and shipping developer experiences: SDKs, CLIs, templates, documentation, and self-service workflows. Measure success by adoption and developer NPS, not feature count.
• Proven ability to lead cross-functional initiatives across 4+ teams without direct authority.
• Strong written communication: strategy documents, PRDs, and executive briefs that drive alignment at VP and CIO level.
• Comfort with ambiguity; will define the roadmap for capabilities that don't exist yet, in a space that is evolving weekly.

Nice to Haves

• Experience with Databricks, Lakehouse architecture, Unity Catalog, MLflow, or Delta Lake.
• Familiarity with LangGraph, LangChain, or similar agent orchestration frameworks.
• Familiarity with MCP (Model Context Protocol), A2A (Agent-to-Agent), or AG-UI protocols.
• Experience building AI evaluation frameworks: LLM-as-judge, red-teaming, or automated quality scoring.
• Experience with design systems, component libraries, or frontend platform work.
• Background in enterprise SaaS platform consolidation or migration.