Staff AI Product Manager

Robots & Pencils is an applied AI engineering firm building the next frontier of business architecture. We design and ship AI co-workers that integrate into enterprise operations and deliver measurable results for our clients. We're all in on AWS, combining deep UX capability with senior engineering talent to get AI into production fast and keep it there. 
We’ve earned the trust of leaders across Consumer Products and Retail, Education, Energy, Financial Services, Healthcare, and Manufacturing and more, and earned a reputation as the nimble alternative to traditional global systems integrators. Founded in 2009, with delivery centers in Canada, the United States, Eastern Europe, and Latin America, we are smaller, faster, and more senior by design. Our teams average 15+ years of experience. We move fast, sweat the details, and build things that actually ship. 

Position Overview 

We're looking for a Staff Product Manager, AI to define product vision and drive strategy for enterprise GenAI and agentic AI programs. This role is for a seasoned AI product leader who combines deep technical fluency with executive-level communication skills — someone who thinks in systems, works backwards from the customer problem, and has the hands-on ability to prototype, define agent architectures, and own production-grade AI outcomes end-to-end. 

In this role, you will work as the senior product authority on a cross-functional AI delivery team, setting direction, driving cross-functional alignment, and owning initiative-level outcomes from discovery through scale. You'll partner with AWS solution architects, client executives, and engineering leads to define the AI product decisions that move enterprise organizations from promising demos to production systems that deliver measurable business value. 

Why This Role Matters 

Staff Product Managers, AI are the bridge between what enterprise AI can actually do and what we actually ship at Robots and Pencils. On the engagements you lead, you set the direction, frame the right problems, and make the multi-model architecture and evals trade-off calls that decide whether the AI product is production-grade or just a demo. We ship production-ready AI in 30 to 45 days; that pace holds only when someone keeps the customer in the room, sweats the details that decide whether agents survive contact with real users, and refuses to let demo-grade speed beat production-grade clarity. 

What You’ll Do 

Craft & Delivery 

• Define and drive the product vision, strategy, and roadmap for GenAI and agentic AI solutions, connecting AI capabilities to enterprise business outcomes with explicit invest vs. defer tradeoffs  

• 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  

• 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  

• Define and own agent behavior end-to-end — system prompts, tool schemas, context management (summarization, working memory, information flow across multi-step tasks), human-in-the-loop boundaries, guardrails, and failure recovery patterns — partnering with engineering on the design decisions that shape how agents actually behave in production 

• Build AI prototypes to validate hypotheses and de-risk product decisions before engineering commits to full build 

• Own backlog management and sprint execution day-to-day across the engagement — prioritizing across multiple AI workstreams, refining agent behavior and requirements with engineering, and keeping the team moving at production pace. 

• Establish and own agent evaluation frameworks covering task completion, reasoning quality, tool selection, failure recovery, safety, and responsible AI — and partner with engineering on production readiness, including observability, drift detection, and prompt versioning 

• Define agent-level success metrics spanning quality, cost, and trust — such as task completion rate, cost per task (not per inference), and escalation rate 

• Define the metrics framework including north star, input, and guardrail metrics at the initiative level and report product impact to leadership  

• Create deployment playbooks, reference architectures, and knowledge transfer materials so teams can sustain solutions independently after engagement  

• Balance near-term deployment milestones with long-term platform scalability and sustainability  

• Bring an AI-forward mindset to your daily work, using tools like Claude, Claude Code, Cursor, and GitHub Copilot — and agentic frameworks as a core part of how product strategy gets built and validated (e.g., LangChain, LangGraph, AWS Strands) 

Collaboration & Communication 

• Build trusted relationships with client executives and 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, and set realistic expectations at the executive level 

Leadership & Influence 

• Establish AI product delivery standards, evals practices, and agent design patterns on the engagements you support  

• Mentor senior and mid-level AI PMs, helping them grow their technical depth, product judgment, and client advisory skills  

• Own the most difficult AI product decisions on your engagement, serving as the senior decision-maker on scope, prioritization, HITL boundaries, and what's good enough to ship 

• Monitor the competitive GenAI landscape and emerging agentic patterns to continuously inform roadmap and technology decisions  

• Contribute to business development, proposals, and the growth of R&P's AI product practice 

What You’ll Bring 

• 8+ years in product management, forward deployment, or solutions engineering — must have shipped AI products from prototype through production at enterprise 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. 

• Ability to read and engage deeply with code (Python, JavaScript, TypeScript), evaluate architectures, and make product tradeoffs on complex technical constraints  

• Deep GenAI fluency across LLMs, RAG, fine-tuning, prompt engineering, context engineering, and evals — with hands-on experience building or shipping agentic systems including planning, tool use, human-in-the-loop controls, and guardrails  

• Proven ability to prototype AI solutions using AI tools to validate hypotheses and de-risk product decisions (e.g., Claude, Claude Code, Cursor, GitHub Copilot)