Paden Gayle

Paden Gayle is a forward deployed AI engineer at Notion and the author of The Forward Deployed Engineer, an O’Reilly handbook available in early release. He focuses on enterprise AI deployment: discovering how a customer’s work actually happens, building within its data and security requirements, and leaving software its team can operate. His writing treats integrations, approval decisions, and the eventual handoff as part of the engineer’s responsibility for a customer’s outcome.

From cello to customer systems

Paden initially pursued a career as a cellist, attending Juilliard before leaving after one semester. He moved into business studies and graduated from Baruch College’s Zicklin School of Business in 2019 with a degree in computer and information systems. As his interest in coding grew, he founded the college’s Association for Information Systems chapter and tutored programming, statistics, and data mining. Teaching began as a way to support himself through college; helping someone understand a difficult concept became a reward of its own. His Baruch alumni profile traces that transition into engineering and career coaching.

Paden began his technical career at Accenture as a technical architect in 2019. At Bloomberg, he built trading systems and reworked a data pipeline that supplied BloombergGPT. A part-time contract at Scale AI involved evaluating model responses; later work at Google concerned certificate and identity infrastructure behind TLS and public-key infrastructure. He subsequently moved into forward deployed engineering at Notion. His account of that work spans customer delivery, financial software, model evaluation, and security infrastructure.

Owning the outcome and designing the handoff

Paden’s account of forward deployed engineering makes responsibility for a named customer central to the role. Demonstrating a platform is only part of the work: the engineer also handles the integrations, migrations, and custom software needed to make it useful in the customer’s environment.

That responsibility shapes several practical decisions:

• Discover the existing workflow. Start with the people who need the result and the systems they use, including undocumented integrations and critical spreadsheets. A small implementation using the customer’s own data can test whether a useful path exists before the team commits to more infrastructure.
• Choose whether software is needed. His handbook covers discovery, scoping, governed data, agent design, and security reviews, including the choice between an agent, a simpler worker, and no new software. The customer’s problem determines how much to build.
• Design the departure. Discard early scaffolding when it has served its purpose. The remaining system should run without its original builder, and the customer’s team should be able to maintain and extend it. A successful handoff is part of delivery.

Making approval decisions part of the build

In Deploying AI Inside a Bank Is Not an Engineering Problem, Paden uses a hypothetical document exception assistant to explain why a convincing demonstration can still be impossible to approve. The unresolved questions are concrete: where the system runs, who authorized access to the documents, who reviews an incorrect exception, and which existing controls the workflow must satisfy. He recommends finding the people who own those decisions in the first week and defining the smallest useful system they can evaluate.

When a customer cannot provide a complete specification, Paden’s method is to shadow the work, draft a workflow, and narrow it with the people responsible for its controls. Start with the least authority needed: a drafting assistant can produce evidence about errors, human overrides, and recovery before it receives permission to take further actions. This gives reviewers something specific to assess when deciding whether to expand its authority.

His proposed evidence pack records intended use, data lineage, failure modes, results by segment, and accountable controls. Original inputs and outputs matter because rerunning a model later cannot reconstruct precisely what happened. Segment-level results matter because a strong aggregate score can hide a poorly supported document type or user group; those weak areas need explicit restrictions. The bank example illustrates his deployment method rather than documenting a shipped banking project.

Paden also runs Applied AI Engineers and teaches through Maven and O’Reilly. His forward deployed engineering training uses scoping exercises, project reviews, and interview practice to help engineers practice the decisions the role demands alongside coding. It extends his earlier teaching and coaching work into a profession where understanding the customer’s workflow, choosing a manageable scope, and preparing a maintainable handoff are central skills.