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
| Need |
Skill |
| Commercial/enterprise AI architecture |
applied-ai-architect-commercial-enterprise |
| Build and ship AI features |
ai-engineer |
| AI ops, incidents, rollout governance |
ai-lead-ops |
| Risk tiering and policy |
ai-risk-governance |
| Red-team before major launch |
ai-redteam |
| Token/cost improvement program |
ai-token-improvement-plan-engineer |
| Vertical fullstack delivery |
senior-fullstack-developer |
| Product analytics data |
analytics-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
1---2name: engineering-manager-vertical-ai-products3description: 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.4---56# Engineering Manager, Vertical AI Products78## When to Use910- Build or scale **vertical AI product engineering** (domain squads, not central platform only)11- Prioritize vertical backlog with **PM, GTM, and domain SMEs**12- Staff and develop **AI engineers, fullstack, and tech leads** on vertical lines13- Run **launch governance** for customer-facing AI (eval, risk tier, kill switch)14- Resolve **platform vs vertical** build conflicts and shared component roadmaps15- Set **team KPIs** (ship cadence, eval regression, incidents, cost per vertical)16- Escalate vertical-specific compliance or data-boundary issues1718## When NOT to Use1920- AI solution architecture and multi-tenant ADRs → `applied-ai-architect-commercial-enterprise`21- Implement RAG, agents, eval code → `ai-engineer`22- AI production ops, vendor SLOs, org-wide model release process → `ai-lead-ops`23- AI policy registers and regulatory mapping → `ai-risk-governance`24- Analytics engineering and dbt marts → `analytics-data-engineering-manager-product`25- Company-wide non-AI programs → `technical-program-manager`26- UX research and interaction design → `product-designer`2728## Related skills2930| Need | Skill |31|---|---|32| Commercial/enterprise AI architecture | `applied-ai-architect-commercial-enterprise` |33| Build and ship AI features | `ai-engineer` |34| AI ops, incidents, rollout governance | `ai-lead-ops` |35| Risk tiering and policy | `ai-risk-governance` |36| Red-team before major launch | `ai-redteam` |37| Token/cost improvement program | `ai-token-improvement-plan-engineer` |38| Vertical fullstack delivery | `senior-fullstack-developer` |39| Product analytics data | `analytics-data-engineering-manager-product` |4041## Core Workflows4243### 1. Vertical squad org design4445Hub platform vs vertical pods; domain SME interfaces; ratios.4647**See `references/vertical_team_org.md`.**4849### 2. Roadmap and vertical bets5051Platform leverage vs bespoke vertical logic; capacity and bet sizing.5253**See `references/vertical_roadmap_prioritization.md`.**5455### 3. AI feature launch governance5657Eval gates, risk tier, rollback, hypercare — coordinate with ops and risk.5859**See `references/ai_feature_launch_governance.md`.**6061### 4. Stakeholder partnerships6263PM, sales, solutions, legal, horizontal AI platform.6465**See `references/stakeholder_vertical_partnerships.md`.**6667### 5. Hiring and development6869IC/lead levels for vertical AI product engineering.7071**See `references/hiring_development_vertical.md`.**7273### 6. Team metrics and accountability7475Delivery, quality, safety, unit economics by vertical.7677**See `references/team_metrics_vertical_ai.md`.**7879## Output standards8081- Roadmap items: **vertical outcome**, **AI capability**, **platform dependency**, **owner**82- No GA without signed eval/risk checklist for customer-facing AI83- Escalations include trade-offs (scope, date, platform build vs fork)84- Architecture changes route through `applied-ai-architect-commercial-enterprise`8586## When to load references8788- **Org** → `references/vertical_team_org.md`89- **Roadmap** → `references/vertical_roadmap_prioritization.md`90- **Launch** → `references/ai_feature_launch_governance.md`91- **Stakeholders** → `references/stakeholder_vertical_partnerships.md`92- **People** → `references/hiring_development_vertical.md`93- **KPIs** → `references/team_metrics_vertical_ai.md`
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