Technical Product Manager

Role overview

Robots & Pencils creates scalable digital products that address meaningful business challenges. This position is for a Staff Product Manager who is deeply experienced with Generative AI and agentic systems, and who can also build practical solutions hands-on while owning product outcomes from concept to delivery. You will be responsible for initiative-level results, stakeholder confidence, and helping shape the company’s AI product practice. The ideal person thinks in systems, starts from customer pain points, and stays curious about the fast-changing AI landscape.

This role focuses on a GenAI program inside the AWS ecosystem. The objective is to help enterprise teams move from impressive demos to production-ready agent systems that can operate at scale, satisfy security and compliance needs, and create measurable business value. You will help define the evals, tooling, patterns, and reference architectures that make deployment repeatable. The working style is to validate early, test assumptions quickly, and document as you build.

Product strategy and AI direction

• Set the product vision, strategy, and roadmap for GenAI offerings, with agentic AI—such as orchestration, tool use, and multi-step workflows—as the central focus, and connect those capabilities to enterprise outcomes.
• Convert enterprise problems into clear product requirements, and turn feature requests into outcome-based priorities with explicit choices about what to pursue now and what to postpone.
• Manage the balance between short-term delivery goals and the need for a scalable, sustainable platform over the long run.
• Track the competitive GenAI space and new agentic design patterns to guide roadmap and technology decisions.

Discovery and validation

• Study how enterprise users work with AI agents and where trust breaks down; identify the highest-risk assumptions and turn them into testable hypotheses first.
• Plan and run experiments such as proofs of concept, pilot rollouts, and scenario-based testing for multi-step tasks, edge cases, and recovery from failures, especially where non-deterministic outputs make standard QA methods insufficient.
• Turn research findings, experiment results, and market intelligence into practical insights that support product success.

Agent design, prototyping, and production

• Define agent behavior and prototype prompts and tool schemas; work with engineering on context handling, including summarization, working memory, and information flow across multi-step work.
• Lead product tradeoff discussions for multi-model systems by defining quality, cost, and latency targets that determine which model should be used at each step of the workflow.
• Create AI prototypes to test assumptions, and define when human review is needed, when the agent can act on its own, when it should escalate, and how to manage uncertain outputs.
• Build evaluation frameworks for agents, including task completion, reasoning quality, tool choice, failure recovery, and safety, and partner with engineering on readiness concerns such as observability, drift, responsible AI, and prompt version control.
• Set success metrics at the agent level, including task completion rate, cost per task, escalation rate, time to resolution, and customer trust, along with business KPIs.

Delivery and execution

• Own the full product lifecycle from discovery through staged releases, define the metric framework with north star, input, and guardrail measures, and report product impact to leadership.
• Manage backlog, scope, dependencies, and risks; lead agile ceremonies and create strong PRDs, product briefs, and decision records.
• Assess technology and platform choices from a product standpoint, and create deployment playbooks, reference architectures, and knowledge-transfer materials so teams can operate the solutions independently.
• Use AI to speed up product work such as research, analysis, prototyping, and documentation, while applying judgment about when human review is required; quickly learn new domains and support teammates across the initiative.

Stakeholder management

• Develop trusted relationships with executives and stakeholders, acting as the main product advisor for AI direction and deployment strategy.
• Work alongside AWS Solution Architects and account teams to align technical direction, service selection, and go-to-market planning for GenAI solutions.
• Set clear expectations around scope, timing, and tradeoffs, and guide decisions across competing priorities using evidence, alternatives, and sound reasoning.
• Explain AI strengths and limitations to non-technical audiences, manage overhyped expectations, and uncover unmet needs that strengthen the relationship and expand the account.

Required background

• 8 to 12+ years of experience in product management, forward deployment, or solutions engineering, with a track record of taking AI products from prototype to production at scale.
• Strong product judgment and the ability to determine what matters most for users and the business, prioritize with incomplete information, and shape products around real outcomes.
• Deep understanding of GenAI, including LLMs, RAG, fine-tuning, prompt engineering, context engineering, and evals, plus hands-on experience building or launching agentic systems with planning, tool use, human-in-the-loop controls, and guardrails.
• Proven ability to prototype AI solutions using tools such as Cursor, Claude, and Copilot to test assumptions and reduce product risk.
• Experience delivering AI solutions in enterprise settings with strong technical fluency, including reading code, assessing architectures, making product choices under technical constraints, and driving scalable deployment patterns.
• Excellent communication skills with the ability to write clear PRDs, technical specifications, and decision logs, and to align Directors, VPs, and C-level partners across the product lifecycle.
• Comfort working in ambiguous, fast-moving environments where AI capabilities and best practices change rapidly.
• Working knowledge of the AWS AI stack, including Bedrock, AgentCore, SageMaker, Strands, Kendra, OpenSearch, Lambda, and Step Functions, to support informed product and architecture decisions.

Preferred background

• Background in software engineering or coding, especially Python, JavaScript, or TypeScript.
• Experience delivering work in an agency or consulting environment.
• Exposure to Financial Services, Healthcare, or Life Sciences.
• Familiarity with open-source LLMs such as Llama and Mistral for flexibility and cost control.
• Experience running time-boxed discovery efforts or technical spikes with fast validation cycles.

Why this role stands out

You will work where advanced AI meets real enterprise impact, helping clients deploy Generative and Agentic AI solutions that change how they operate. The environment combines the variety of consulting—new industries, new problems, new technologies—with the depth of a product role, so you will build, ship, and measure rather than only advise. The team is collaborative, technically strong, and committed to delivering excellent client work.

Additional information

Location: Seattle, WA. Employment type: full-time, onsite. No stipend or salary amount was provided in the source.