Robots & Pencils招聘技术产品经理（美国）| LinkedIn

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<br/><br/>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.<br/><br/><strong>Key Responsibilities</strong><br/><br/><strong>Product Strategy & AI Vision</strong><br/><br/><ul><li>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</li><li>Translate enterprise problems into structured product requirements; reframe feature requests into outcome-driven priorities with explicit tradeoffs on invest in vs. defer</li><li>Balance near-term deployment milestones with long-term platform scalability and sustainability</li><li>Monitor the competitive GenAI landscape and emerging agentic patterns to inform roadmap and technology decisions</br/><br/></li></ul><strong>Discovery & Validation</strong><br/><br/><ul><li>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</li><li>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</li><li>Distill research, experiments, and competitive intelligence into clear insights that pave the path for a successful product</li><li>Agent Design, Prototyping & Production</li><li>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</li><li>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</li><li>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</li><li>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)</li><li>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</li><li>Delivery & Execution</li><li>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</li><li>Manage the product backlog, scope, dependencies, and risks; drive agile ceremonies and produce high-quality PRDs, product briefs, and decision logs</li><li>Evaluate technology and platform decisions from a product perspective; create deployment playbooks, reference architectures, and knowledge transfer materials so teams sustain solutions independently</li><li>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</br/><br/></li></ul><strong>Stakeholder Management</strong><br/><br/><ul><li>Build trusted relationships with stakeholders and executives; serve as the go-to product advisor and primary contact for AI product direction and deployment strategy</li><li>Partner with AWS Solution Architects and account teams to align on technical approach, service selection, and go-to-market for GenAI solutions</li><li>Manage expectations on scope, timelines, and tradeoffs; facilitate decisions across competing priorities using data, alternatives, and clear rationale</li><li>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</br/><br/></li></ul><strong>Required Skills</strong><br/><br/><ul><li>8-12+ years in product management, forward deployment, or solutions engineering; must have shipped AI products from prototype through production at scale</li><li>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</li><li>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)</li><li>Proven ability to prototype AI solutions using AI tools (Cursor, Claude, Copilot) to validate hypotheses and de-risk product decisions</li><li>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</li><li>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</li><li>Comfortable operating in ambiguous, fast-moving environments where the AI landscape evolves weekly</li><li>PM-level fluency across the AWS AI ecosystem - Bedrock, AgentCore, SageMaker, Strands, Kendra, OpenSearch, Lambda, Step Functions - to make informed product and architecture decisions</br/><br/></li></ul><strong>Preferred Qualifications</strong><br/><br/><ul><li>Software engineering or coding background (Python, JavaScript, TypeScript)</li><li>Agency or consulting delivery experience</li><li>Experience in Financial Services, Healthcare, or Life Sciences industries</li><li>Familiarity with open-source LLM ecosystem (Llama, Mistral) for flexibility and cost optimization</li><li>Prior experience leading time-boxed discovery initiatives or technical spikes with rapid validation cycles</br/><br/></li></ul><strong>Why Join R&P?</strong><br/><br/>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.

8-12+年产品管理、向前部署或解决方案工程经验；必须有从原型到大规模生产的产品经验
强的产品洞察力 - 能够识别用户和业务中真正重要的是什么，用不完整的信息做出优先级决策，并塑造能带来实际成果的产品
深入的生成式和代理式AI能力 - LLMs，RAG，微调，提示工程，上下文工程，评估 - 有构建或发布代理系统（规划，工具使用，HITL，护栏）的实践经验
已证明能够使用AI工具（Cursor，Claude，Copilot）原型AI解决方案来验证假设并降低产品决策风险
在企业环境中部署AI解决方案的经验，具有强大的技术能力 - 能够阅读代码，评估架构，就技术限制做出产品决策，并推动可扩展的部署模式
出色的沟通能力 - 清晰的PRD、技术规格和决策日志；有领导AI产品全生命周期并推动与总监、副总裁和C级领导层对齐的经验
在模糊、快速变化的环境中操作的舒适度，其中AI景观每周都在演变
AWS AI生态系统（Bedrock，AgentCore，SageMaker，Strands，Kendra，OpenSearch，Lambda，Step Functions）的PM级能力，以做出有根据的产品和架构决策

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