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设计与创意 · SKILL.md

engineering-manager-vertical-ai-products

engineering-manager-vertical-ai-products

【设计与创意】Guides engineering managers leading vertical AI product teams—industry or domain-specific copilots and AI features (not horizontal platform)—org design, hiring, roadmap with PM and GTM, vertical launch governance (eval, safety, cost), squad capacity, and escalation across shared AI platform and domain experts. Use when managing engineers shipping ver…

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name: engineering-manager-vertical-ai-products description: | Guides engineering managers leading vertical AI product teams—industry or domain-specific copilots and AI features (not horizontal platform)—org design, hiring, roadmap with PM and GTM, vertical launch governance (eval, safety, cost), squad capacity, and escalation across shared AI platform and domain experts. Use when managing engineers shipping vertical AI products, prioritizing domain AI backlogs, staffing vertical squads, or aligning AI feature GA with sales and compliance—not for AI architecture ADRs (applied-ai-architect-commercial-enterprise), hands-on RAG/agents (ai-engineer), AI platform SRE and model rollout ops (ai-lead-ops), analytics marts (analytics-data-engineering-manager-product), or generic TPM programs (technical-program-manager). For agent prompt and eval harness work (not people management), use prompt-engineer-agent-prompts-evals. For managing the prompt/eval function (hiring, golden CI policy, judge program), use engineering-manager-agent-prompts-evals.

Engineering Manager, Vertical AI Products

When to Use

  • Build or scale vertical AI product engineering (domain squads, not central platform only)
  • Prioritize vertical backlog with PM, GTM, and domain SMEs
  • Staff and develop AI engineers, fullstack, and tech leads on vertical lines
  • Run launch governance for customer-facing AI (eval, risk tier, kill switch)
  • Resolve platform vs vertical build conflicts and shared component roadmaps
  • Set team KPIs (ship cadence, eval regression, incidents, cost per vertical)
  • Escalate vertical-specific compliance or data-boundary issues

When NOT to Use

  • AI solution architecture and multi-tenant ADRs → applied-ai-architect-commercial-enterprise
  • Implement RAG, agents, eval code → ai-engineer
  • AI production ops, vendor SLOs, org-wide model release process → ai-lead-ops
  • AI policy registers and regulatory mapping → ai-risk-governance
  • Analytics engineering and dbt marts → analytics-data-engineering-manager-product
  • Company-wide non-AI programs → technical-program-manager
  • UX research and interaction design → product-designer

Related skills

NeedSkill
Commercial/enterprise AI architectureapplied-ai-architect-commercial-enterprise
Build and ship AI featuresai-engineer
AI ops, incidents, rollout governanceai-lead-ops
Risk tiering and policyai-risk-governance
Red-team before major launchai-redteam
Token/cost improvement programai-token-improvement-plan-engineer
Vertical fullstack deliverysenior-fullstack-developer
Product analytics dataanalytics-data-engineering-manager-product

Core Workflows

1. Vertical squad org design

Hub platform vs vertical pods; domain SME interfaces; ratios.

See references/vertical_team_org.md.

2. Roadmap and vertical bets

Platform leverage vs bespoke vertical logic; capacity and bet sizing.

See references/vertical_roadmap_prioritization.md.

3. AI feature launch governance

Eval gates, risk tier, rollback, hypercare — coordinate with ops and risk.

See references/ai_feature_launch_governance.md.

4. Stakeholder partnerships

PM, sales, solutions, legal, horizontal AI platform.

See references/stakeholder_vertical_partnerships.md.

5. Hiring and development

IC/lead levels for vertical AI product engineering.

See references/hiring_development_vertical.md.

6. Team metrics and accountability

Delivery, quality, safety, unit economics by vertical.

See references/team_metrics_vertical_ai.md.

Output standards

  • Roadmap items: vertical outcome, AI capability, platform dependency, owner
  • No GA without signed eval/risk checklist for customer-facing AI
  • Escalations include trade-offs (scope, date, platform build vs fork)
  • Architecture changes route through applied-ai-architect-commercial-enterprise

When to load references

  • Org → references/vertical_team_org.md
  • Roadmap → references/vertical_roadmap_prioritization.md
  • Launch → references/ai_feature_launch_governance.md
  • Stakeholders → references/stakeholder_vertical_partnerships.md
  • People → references/hiring_development_vertical.md
  • KPIs → references/team_metrics_vertical_ai.md
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