Private Draft

The 29 personas behind AI

We’ve organized every stage and persona in the AI supply chain, informed by real recruiting at frontier companies. Click any row to see matching profiles from our talent graph.

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Forward-Deployed

Ships in customer codebases

Forward-Deployed

Summary

Known as: Forward-Deployed Engineer, Field Engineer, Deployment Engineer, Systems Integration Engineer, Customer Engineer

Post-sale implementation and high-stakes integration for enterprise customers with complex systems — both the engineers writing code in client environments and the embedded PMs navigating delivery against messy business-process constraints. For physical systems, includes on-site deployment, hardware-software validation, and systems integration. Engagement depth varies: the largest programs can be long-running and deeply embedded, while many teams now run time-boxed pods across a portfolio of accounts as tooling and best practices evolve quickly.

Specializations

Embedded Delivery PM (Implementation PM) — A PM variant embedded on specific customer accounts to ship enterprise-grade AI systems (often agents) in real workflows. Discovers and translates requirements, navigates messy business-process constraints with engineering counterparts, and iterates system behavior to handle real volumes safely and reliably. Shapes the roadmap based on deployment realities and customer outcomes, not just feature prioritization. Sometimes titled "Forward-Deployed PM" or "Agent PM," but the underlying task bundle is customer-embedded delivery.

Technical Account Manager (TAM) — Post-sale technical ownership for customer outcomes and expansion. Owns executive relationships, ROI/value modeling, QBRs, and cross-functional coordination to keep deployments healthy and growing. Often paired with forward-deployed delivery when implementations are complex or strategic, and sometimes split into adjacent roles depending on org design.

Forward-deployed work often sits in tension with Product. Forward-deployed teams prioritize customer outcomes and delivery timelines, while Product prioritizes reuse and long-term leverage. The best teams do both: ship what the customer needs, then extract reusable integration patterns, connectors, and playbooks that make the next deployment faster. Forward-deployed engineers are typically expected to write code or configure systems on customer infrastructure, navigate messy data and constraints, and ship end-to-end outcomes with more ambiguity than classic Solutions Engineering. In practice, forward-deployed engineers are hands-on builders in the customer environment (and in some orgs, they also support late-stage deals where integration risk is the blocker). In agentic AI, this is often "agent onboarding": wiring context (data, tools, permissions), integrations, and workflows so the system is reliable end-to-end. Their work should compound over time into reusable integrations and plugin patterns, rather than starting from scratch on every account.

Where the Work Lives

[1]Substrate

[2]Compute

[3]Intelligence

[4]Systems

Secondary

Integrates AI into customer environments, navigating messy infrastructure and data constraints.

[5]Distribution

Primary

Ships outcomes directly to end users and enterprise customers in their own codebases.

Candidate Archetypes