Daniel Grillot
Principal Engineer
I build production AI into engineering systems at enterprise scale—not demos, not slideware.
Nineteen years shipping software. Lately: principal-level platform and AI-workflow work in a large public media company—security and scale as hard constraints, not afterthoughts.
Where I create leverage
Scope and history—employers, titles, and outcomes—are on LinkedIn. This page is the short version of how I work.
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AI in the delivery loop
I design how generative AI shows up across architecture, development, testing, analysis, documentation, and tooling—so it becomes part of how engineering ships, not a side experiment. That includes factory-style AI engineering systems: standards, workflows, and guardrails teams can actually run.
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Adoption that clears security
I evaluate and integrate these systems with security, reliability, scalability, and protection of proprietary data as first-class constraints. The bar for a public company is not the bar for a weekend demo.
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Ambiguous, cross-org problems
I take high-priority initiatives from concept through architecture and production—especially the ones that cut across teams. Organizations with strong engineers still need someone who raises the ceiling on the hardest work.
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Platforms already at scale
I have operated services and platforms for audiences in the tens of millions, and auth/API gateways at millions of requests per minute. New capability still has to live inside systems that cannot go down casually.
Where I do my best work
Large organizations where AI-assisted engineering is strategic, security and platform partners are real stakeholders, and judgment under constraint matters as much as fluency with the latest model. Best fit: a principal seat with ownership of that kind of work—not a novelty pilot.
Contact
For principal-level conversations about fit—send a note.