AI Product Manager

The candidate will have responsibilities across the following functions:

AI Product Manager:

• Defines a clear vision and roadmap for AI features or products, ensuring they address real user problems and deliver tangible business outcomes, not merely model deployment.
• Strong AI/ML literacy, including understanding of model types, training vs. inference, data quality considerations, limitations, and explainability to drive informed trade-offs.
• Data and metrics fluency, including knowledge of data sources, labelling processes, evaluation metrics (accuracy, precision/recall, hallucination rates), and clear linkage to product KPIs.
• Works closely with legal and privacy teams to manage bias, safety, and compliance, and designs fallback experiences when AI outputs are uncertain or incorrect.

PM Core:

• Tech Decision-Making: Evaluates tech options using cost, risk and timeline criteria; can challenge engineering proposals with informed questions; understands API and data trade-offs.
• Product Craft and Specs: Produces a clear HLD with architecture context; writes an LLD with data flows and edge cases; contributes to product strategy discussions with business cases.
• Analytics and Data-Driven Decisions: Defines success metrics for features; writes basic SQL or uses BI tools; runs simple A/B test analysis.
• Delivery and Stakeholders: Manages backlog independently; runs sprint planning and retros; manages expectations across 2-3 stakeholder groups; prepares exec-ready updates.
• OKRs and Roadmap: Writes squad-level OKRs with measurable key results, maintains a quarterly roadmap, partners with designers on user flows, and translates research into requirements.

AI Products Skills:

• AI-Assisted Product Craft: Uses SpecKit to generate PRDs and specs with editing; builds clickable prototypes using Figma or AI design tools; creates compelling demo scripts.
• AI Tools and Prototyping: Uses Cursor/Claude Code to build simple UI prototypes, evaluates AI tools for squad needs, and writes basic prompts with system instructions and examples.
• Agent and AI Design Literacy: Writes product requirements for agent features; makes informed choices between AI interaction patterns; understands latency and hallucination trade-offs.
• Prompt Engineering: Designs structured prompts with system instructions, examples and guardrails; tests across edge cases; implements function-calling and JSON mode.

Evals and Model Quality:

• Defines eval criteria for product features (accuracy, latency, safety); works with engineers to build eval datasets; reviews eval results.
• AI Model and Cost Literacy: Tracks model costs per feature; makes model-tier decisions based on use case; defines fine-tuning objectives and success criteria with engineers.
• AI Architecture and Governance: Writes requirements for RAG and multi-agent systems; implements safety guardrails in product specs; conducts basic red-teaming; ensures data compliance.