Technical Product Manager

Key Responsibilities

Product Strategy & AI Vision

• Define and drive the product vision, strategy, and roadmap for GenAI solutions - with agentic AI (agent orchestration, tool use, multi-step workflows) as the primary focus - connecting AI capabilities to enterprise business outcomes
• Translate enterprise problems into structured product requirements; reframe feature requests into outcome-driven priorities with explicit tradeoffs on invest in vs. defer
• Balance near-term deployment milestones with long-term platform scalability and sustainability
• Monitor the competitive GenAI landscape and emerging agentic patterns to inform roadmap and technology decisions

Discovery & Validation

• Research how enterprise users interact with AI agents and where they lose trust; frame the riskiest assumptions as testable hypotheses and de-risk them first
• Design and run experiments - POCs, pilot deployments, scenario-based testing of multi-step workflows, edge cases, and failure recovery - to validate agentic solutions where non-deterministic output makes traditional QA insufficient
• Distill research, experiments, and competitive intelligence into clear insights that pave the path for a successful product

Agent Design, Prototyping & Production

• Define agent behavior and prototype system prompts and tool schemas; partner with engineering on context management - summarization, working memory, and information flow across multi-step tasks
• Drive multi-model architecture tradeoffs with engineering - define the quality, cost, and latency targets that determine which model serves each step in the agent workflow
• Build AI prototypes to validate hypotheses; define human-in-the-loop boundaries and guardrails - when the agent acts autonomously, when it escalates, and how to handle non-deterministic output
• Establish agent evaluation frameworks - task completion, reasoning quality, tool selection, failure recovery, safety - and partner with engineering on production readiness (observability, drift, responsible AI, prompt versioning)
• Define success metrics at the agent level - task completion rate, cost per task (not per inference), escalation rate, time to resolution, and customer trust alongside business KPIs

Delivery & Execution

• Own the end-to-end product lifecycle from discovery through phased rollouts; establish the metrics framework (north star, input, guardrail metrics) and report product impact to leadership
• Manage the product backlog, scope, dependencies, and risks; drive agile ceremonies and produce high-quality PRDs, product briefs, and decision logs
• Evaluate technology and platform decisions from a product perspective; create deployment playbooks, reference architectures, and