Technical Product Manager
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.
Apply To This Job
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.
Apply To This Job