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Technical Product Manager at Robots and Pencils. . Location: US - Remote. At Robots & Pencils, we build meaningful, scalable digital products that solve real business problems. 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 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 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. . . Why Join R&P?. . You'll work at the intersection of cutting-edge AI and real enterprise impact - helping clients deploy Generative and Agentic AI solutions that change how their businesses operate. R&P gives you the variety of consulting (new problems, new industries, new tech) with the depth of a product role - you'll build, ship, and measure, not just advise. The team is collaborative, technically sharp, and genuinely invested in doing great work for clients.. .