We are looking for a Staff Product Manager who combines deep Generative and Agentic AI fluency with hands-on building ability to own AI product outcomes end-to-end. As a Staff PM, you're accountable for initiative-level outcomes, stakeholder satisfaction, and contributing to R&P's AI product practice. You think in systems, work backwards from the customer problem, and stay relentlessly curious about what's next in AI
Enterprise clients want to deploy Agents - moving from a promising demo to a production system that works at scale, meets security and compliance requirements, and delivers measurable business value is hard. This role owns that problem. You'll be part of a GenAI initiative within the AWS ecosystem, building the evals, tools, patterns, and reference architectures that make AI deployment repeatable. The mindset: prove it works, test assumptions early, and document while building.
Key ResponsibilitiesProduct 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 knowledge transfer materials so teams sustain solutions independently
- Use AI to accelerate product work - research, analysis, prototyping, documentation - with judgment on when it needs human oversight; onboard rapidly to new domains and support team members across the initiative
Stakeholder Management- Build trusted relationships with stakeholders and executives; serve as the go-to product advisor and primary contact for AI product direction and deployment strategy
- Partner with AWS Solution Architects and account teams to align on technical approach, service selection, and go-to-market for GenAI solutions
- Manage expectations on scope, timelines, and tradeoffs; facilitate decisions across competing priorities using data, alternatives, and clear rationale
- Frame AI capabilities and limitations for non-technical stakeholders - manage hype cycles, set realistic expectations; surface unmet needs that deepen relationships and grow the account
Required Skills- 8-12+ years in product management, forward deployment, or solutions engineering; must have shipped AI products from prototype through production at scale
- Strong product sense - ability to identify what matters to users and the business, make prioritization calls with incomplete information, and shape products that deliver real outcomes
- Deep GenAI fluency - LLMs, RAG, fine-tuning, prompt engineering, context engineering, evals - with hands-on experience building or shipping agentic systems (planning, tool use, HITL, guardrails)
- Proven ability to prototype AI solutions using AI tools (Cursor, Claude, Copilot) to validate hypotheses and de-risk product decisions
- Experience deploying AI solutions in enterprise environments with strong technical fluency - can read code, evaluate architectures, make product tradeoffs on technical constraints, and drive scalable deployment patterns
- Exceptional communicator - clear PRDs, technical specs, and decision logs; has led AI products through full lifecycle and driven alignment with Directors, VPs, and C-level
- Comfortable operating in ambiguous, fast-moving environments where the AI landscape evolves weekly
- PM-level fluency across the AWS AI ecosystem - Bedrock, AgentCore, SageMaker, Strands, Kendra, OpenSearch, Lambda, Step Functions - to make informed product and architecture decisions
Preferred Qualifications- Software engineering or coding background (Python, JavaScript, TypeScript)
- Agency or consulting delivery experience
- Experience in Financial Services, Healthcare, or Life Sciences industries
- Familiarity with open-source LLM ecosystem (Llama, Mistral) for flexibility and cost optimization
- Prior experience leading time-boxed discovery initiatives or technical spikes with rapid validation cycles
