How do I write an AI Product Manager CV without prior AI experience?

Don't fake it. AI PM panels — especially at research-led companies like OpenAI London, Anthropic London or DeepMind — catch retrofitted AI experience in the first technical question. Instead, position yourself as a strong PM transitioning into AI, with a credible bridge: shipped one AI feature in your current role (even small), maintained a running eval set, partnered with an ML engineer on a real launch. The hiring market in 2026 is desperate enough for AI PMs that one credible AI feature plus strong general PM background gets interviews. The wrong move is rebadging non-AI work as AI work — that closes more doors than it opens.

Should an AI PM CV include a portfolio link?

Yes, much more than a standard PM CV. The strongest AI PM portfolios I see are concise: one or two case studies of AI features the candidate actually owned, with the eval methodology, the model decision, the production metric, and what they'd do differently. Avoid AI-generated content showcase pages — panels see those as reverse signals. Avoid linking to your prompts public-repo unless you have permission and unless the prompts are genuinely interesting. The goal of the portfolio link is to give the panel one concrete artefact to discuss in the technical round; pick the AI work you can defend in detail.

How important are technical credentials for AI PM CVs?

Less than candidates fear, more than they assume. Hiring managers care most about whether you can hold your own in a technical conversation about evaluation, model selection and production architecture — not whether you have a CS degree. A bootcamp ML certificate plus shipped AI features beats a CS PhD with no shipped work, every time. That said, a strong technical baseline matters for AI PM specifically: candidates who can credibly read a model card, follow an eval report and disagree with an architecture decision get scored higher than those who defer entirely. If your background is non-technical, build the baseline — Andrej Karpathy's 'Neural Networks: Zero to Hero' video series, deploy a few small LLM apps, sit in on ML team architecture reviews — before applying for senior AI PM roles.

CV Example · Tech · UK 2026

AI Product Manager CV Example UK

AI Product Manager CVs in 2026 are unlike standard PM CVs in three specific ways. First, the work itself reads differently — eval set design, model selection trade-offs, hallucination handling, prompt engineering — and weak candidates obscure that work behind generic PM verbs. Second, hiring panels at OpenAI London, Anthropic London, DeepMind, Synthesia, ElevenLabs, Wayve and the AI-PM teams inside fintech are scanning specifically for evidence of shipped AI features in production, not prototypes or research demos. Third, the credibility signal is technical specificity: a senior AI PM CV that says 'shipped LLM features' will be passed over for one that says 'shipped a RAG-based assistant on Llama 3.1 70B with structured-output validation, reduced hallucination rate from 14% to 3% measured on a 200-prompt eval set'. The CV below is built for that audience.

By Alex · 12-year UK recruiter · Updated April 2026

Example header

Priya Reddy · Senior AI Product Manager · 6 years (3 in AI) · London / Hybrid

Personal statement / Professional summary

Senior AI PM with three years shipping LLM-powered features into B2B SaaS, two years prior PM experience in fintech. Built and operated production AI features for a 400-person company including a customer-support assistant handling 12,000 conversations/week, a structured-data extraction pipeline replacing 14 FTEs of manual review, and an internal eval-and-monitoring stack used across 6 teams. Comfortable in research-heavy environments — partnered with two ML engineers and a research scientist for the last 18 months. Strong on the messy half of AI product work: eval-set design, model selection under cost constraints, hallucination-mitigation strategy, and the safety conversations that derail launches if not handled early.

Bullet point examples

Strong bullets follow the same shape: action verb, specific scope, quantified outcome. Use these as patterns, not as copy-paste templates — the numbers must be your own.

Senior AI PM at B2B SaaS (Series C, 400 staff)

Shipped customer-support AI assistant on Claude Sonnet + RAG, handling 12,000 conversations/week with 78% deflection rate, saving £1.8m projected annual support cost in first year.
Reduced hallucination rate from 14% to 3% on a 200-prompt evaluation set across 8 weeks by introducing structured-output validation, retrieval-quality gating, and a judge-model second pass on edge-case categories.
Killed a planned auto-summarisation feature after 6-week red-team test surfaced a class of high-stakes errors the team couldn't reduce below 9% — wrote a post-mortem now used as the team's release-readiness template.

Evaluation infrastructure

Designed and shipped the team's first end-to-end evaluation pipeline (offline eval + online instrumentation + nightly regression check), now used by 6 product teams across the company.
Built a 350-prompt evaluation set with human-rated rubrics across 9 customer-impact categories; rubric is now the team's single source of truth for any model swap or prompt change.

Model selection and cost

Reduced inference cost by 71% on the support-assistant feature by routing 73% of queries to Haiku and reserving Sonnet for the harder categories, with no measurable drop in deflection rate measured over 4 weeks.
Prototyped Llama 3.1 8B fine-tuning for a structured-data extraction task and shipped it to production after it beat GPT-4o-mini on the eval set at 1/40th the per-token cost.

Cross-functional partnership with research and ML

Partnered weekly with two ML engineers and one research scientist on architecture, eval design and roadmap; jointly authored 4 internal RFCs that shaped the team's 2025 model strategy.
Translated research-team capability roadmap into a 6-quarter product roadmap with explicit conditional bets, re-evaluated every 6 weeks against latest model releases.

Earlier role: PM at UK fintech

Owned onboarding flow at a 200-person fintech, lifting day-7 activation from 28% to 47% across 22,000 new accounts (Series B-C transition).
Ran 6 pricing experiments across two cohorts; 4 shipped, two killed; cumulative revenue impact roughly £600k in first 12 months.

Skills section — what to list

Mirror the skills exactly as they appear in target job ads. The ATS reads this section literally — synonyms hurt match scores.

LLM evaluation (offline eval sets, online instrumentation, regression testing)Prompt engineering (system prompts, few-shot, structured output)RAG architecture (retrieval design, chunk strategy, ranking)Model selection (frontier vs specialised, cost-latency-capability trade-offs)Hallucination mitigation strategyAI safety and red-teamingWorking with research scientists and ML engineersPython (intermediate, sufficient for eval scripts and exploration)SQL (intermediate)Product discovery and customer interviewsPRD and one-pager writingOpenAI / Anthropic / Cohere / open-source model APIsWeights & Biases / LangSmith / HeliconeA/B testing and experiment designRoadmapping under capability uncertainty

AI Product Manager-specific CV mistakes that get you binned

× Saying 'shipped AI features' without naming the model, eval methodology or production metric. Panels treat this as a tell that the candidate hasn't operated production AI.
× Listing tools and frameworks (LangChain, vector DBs, agent frameworks) instead of the decisions you made with them. Tools are commodity in 2026; the trade-off thinking is the story.
× Claiming credit for research or ML engineering work. AI PM panels — especially at research-led companies — catch this within minutes and it ends the conversation.
× No mention of safety thinking. Any senior AI PM CV that doesn't engage with safety considerations gets scored as either naïve or performative.
× Vague summary lines like 'experienced PM building cutting-edge AI products'. Strongest summaries name the specific AI surface (assistants, structured extraction, content generation, agents) and the customer segment.

Common questions

How do I write an AI Product Manager CV without prior AI experience?: Don't fake it. AI PM panels — especially at research-led companies like OpenAI London, Anthropic London or DeepMind — catch retrofitted AI experience in the first technical question. Instead, position yourself as a strong PM transitioning into AI, with a credible bridge: shipped one AI feature in your current role (even small), maintained a running eval set, partnered with an ML engineer on a real launch. The hiring market in 2026 is desperate enough for AI PMs that one credible AI feature plus strong general PM background gets interviews. The wrong move is rebadging non-AI work as AI work — that closes more doors than it opens.
Should an AI PM CV include a portfolio link?: Yes, much more than a standard PM CV. The strongest AI PM portfolios I see are concise: one or two case studies of AI features the candidate actually owned, with the eval methodology, the model decision, the production metric, and what they'd do differently. Avoid AI-generated content showcase pages — panels see those as reverse signals. Avoid linking to your prompts public-repo unless you have permission and unless the prompts are genuinely interesting. The goal of the portfolio link is to give the panel one concrete artefact to discuss in the technical round; pick the AI work you can defend in detail.
How important are technical credentials for AI PM CVs?: Less than candidates fear, more than they assume. Hiring managers care most about whether you can hold your own in a technical conversation about evaluation, model selection and production architecture — not whether you have a CS degree. A bootcamp ML certificate plus shipped AI features beats a CS PhD with no shipped work, every time. That said, a strong technical baseline matters for AI PM specifically: candidates who can credibly read a model card, follow an eval report and disagree with an architecture decision get scored higher than those who defer entirely. If your background is non-technical, build the baseline — Andrej Karpathy's 'Neural Networks: Zero to Hero' video series, deploy a few small LLM apps, sit in on ML team architecture reviews — before applying for senior AI PM roles.