Hire AI Product Managersin 2 weeks
BearPlex AI product managers ship AI features customers love: discovery, prioritization, AI-aware product spec writing, evaluation design, launch and iteration. PMs who understand both AI capabilities and customer outcomes deeply.
What a AI Product Manager actually does at BearPlex
An AI product manager at BearPlex owns the product side of AI features: discovery (which AI features should we build?), prioritization (which first?), spec writing (what exactly does this AI feature do?), evaluation design (how do we know it's good?), launch coordination, and iteration based on production behavior. The role is harder than traditional PM because AI features have probabilistic behavior, evaluation is non-trivial, and the design space is constantly changing as new model capabilities emerge. Our AI PMs combine product judgment with deep AI literacy: they understand what current models can and can't do, design eval rubrics that catch real problems, write specs that engineers can implement, and own the iteration cycle that makes AI features improve over time. They've shipped production AI features for B2B SaaS, fintech, healthcare, and consumer products.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Led AI product strategy for a Series C SaaS: shipped 14 customer-facing AI features over 18 months; AI feature adoption drove 23% net revenue retention improvement.
Senior AI PM for a healthcare AI startup through Series B → C: owned product strategy that took the AI clinical assistant from pilot to deployment across 8 hospital networks.
Led AI feature work for a consumer product with 8M users: shipped AI assistant feature that drove 18% increase in DAU within 60 days of launch.
Owned AI product strategy for a Series B fintech's risk decisioning AI: shipped AI features that improved risk decisions while passing compliance review.
Skills matrix
The capabilities every BearPlex AI Product Manager brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| AI product discovery and validation | Expert | Customer research · Prototype testing · AI capability mapping |
| AI feature spec writing (probabilistic systems) | Expert | Spec frameworks for AI · Acceptance criteria for AI · Eval rubric design |
| Evaluation design and rubric writing | Expert | Eval rubric design · LLM-as-judge calibration · Golden dataset construction |
| AI feature prioritization | Expert | Impact / effort frameworks adapted for AI uncertainty |
| Launch and iteration management | Expert | Phased rollout patterns · Production monitoring · Feedback loop design |
| User research for AI features | Expert | Customer interview techniques · Prototype testing · Field research |
| AI literacy (current capabilities and limits) | Expert | Model capability awareness · Vendor landscape knowledge |
| Cross-functional partnership (engineering, design, data) | Expert | Spec interpretation for engineers · Design partnership |
| AI risk and ethical considerations | Advanced | AI risk frameworks · Bias awareness · Disclosure requirements |
| Production AI metrics design | Expert | Engagement metrics · Quality metrics · Cost metrics · ROI frameworks |
| Stakeholder management for AI initiatives | Expert | Executive communication · Risk communication |
| Sector expertise (B2B SaaS, fintech, healthcare, consumer) | Advanced | Sector regulation knowledge · User research patterns |
How we vet AI product managers
Product interview
60-minute deep-dive on past AI product work. We probe: did the PM ship AI features that customers actually adopted? How did they design evaluation? How did they handle the probabilistic nature of AI features? We screen out PMs who treated AI features as just-another-feature without understanding the unique challenges.
Live spec exercise
We give the candidate a realistic AI feature spec problem and 90 minutes. They must write the spec including acceptance criteria for probabilistic behavior, evaluation rubric, and rollout plan. We're looking for: AI literacy, spec rigor, evaluation thinking.
Customer interview exercise
We simulate a customer interview about an AI feature (us as the customer; PM as the interviewer) with 30 minutes of interview time plus 30 minutes of analysis. PMs who can extract real signal from customer conversations is a key differentiator.
Reference checks + paid trial
Reference checks plus 21-day paid trial. Standard pattern: we don't take PMs off trial until both Hamad and the client team report 'I want this PM on the team next sprint.'
What clients say
“Their AI PM saved us from shipping the wrong AI features. She killed two of our top three roadmap items after a week of customer research that surfaced real adoption problems we hadn't anticipated. The third feature we did ship became our biggest AI success.”
“Best AI evaluation framework I've worked with. Our PM designed the eval rubric that became the template for how our team thinks about AI feature quality across all initiatives.”
“Production AI features have probabilistic behavior that confuses traditional PMs. Our BearPlex PM brought the AI literacy we needed to actually own AI features end-to-end.”
Hiring AI product managers: questions answered
Yes: enough to be effective. Our AI PMs understand current model capabilities and limitations, can read evaluation results critically, can spec features that engineers can actually build, and can have substantive conversations with engineering about trade-offs. They're not engineers, but they're AI-literate.
Yes: common engagement model. The PM joins your product team for the duration of the engagement, attends your standups and design reviews, and works alongside your existing PMs to ship AI features. Goal is augmenting your product capacity for AI work, not replacing your existing function.
B2B SaaS (deepest experience), fintech, healthcare, consumer products. For other sectors we staff PMs with direct sector experience when available. AI product work has sector-specific patterns (regulatory considerations, customer expectations, evaluation rigor) that benefit from sector experience.
Embedded AI PM engagement: $20K-$35K monthly retainer (typically 6-18 months). Per-feature engagement (PM owns one major AI feature launch): $40K-$120K depending on scope. Most successful engagements are embedded for the duration of an active AI roadmap.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. PM roles benefit from time zone overlap with the product team; we have PMs in PST / EST time zones available at premium pricing for US-based engagements requiring more synchronous work.
Yes: central to the role. Customer interviews, prototype testing, behavioral analysis, field research. AI PMs need to understand both customer needs and how customers actually interact with AI features (which often differs from how product team imagines).
Evaluation design is one of the most important parts of the role. Our PMs design golden datasets, calibrate LLM-as-judge rubrics with human review, set up A/B test infrastructure for production validation, and own the metrics that determine whether AI features are working. Evaluation isn't an afterthought; it's part of the product spec.
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