Principal Engineer, AI/ML Security at DigitalOcean · LumiJobs

Responsibilities

One distinctive advantage: DigitalOcean sells the substrate we run on. You will partner with our AI platform organization and build on our own AI platform and inference products—dogfooding that is also the fastest path to enterprise capability. Few architect roles make you the internal design partner to the products you depend on.
This is not an advisory role or a review-board seat. You will write code, build prototypes, publish reference implementations, and pair with engineers on the hard parts. You will also say no—to a design that won't scale, to an agent that shouldn't have write access to the general ledger, to a vendor feature that papers over an architecture problem. Ivory-tower distance is the failure mode we screen against.
Own the AI reference architecture. Define the patterns and standards for how DigitalOcean builds agentic systems—orchestration, tool use, capability boundaries, memory and state, retrieval, evaluation, and observability—and make the governed path the easiest one to take. Standards enforced through tooling and paved paths, not approval boards.
Build alongside the team. Design, prototype, and code the hardest and most ambiguous components, and publish reference implementations others build on. Recent hands-on building is the core of this job, not a nice-to-have.
Architect the internal AI platform. Shape the shared services every agent depends on: model access and routing, agent runtimes, evaluation harnesses, durable orchestration for long-running stateful workflows that pause and resume across days, and the developer experience that makes all of it self-service.
Design the tool and capability layer. Define how agents discover and invoke capabilities, built on open standards such as the Model Context Protocol: versioned, schema-defined tools with clear ownership, and evaluation gates before anything becomes available for reuse.
Design how agents meet enterprise systems. Establish the integration, identity, and authorization patterns that let agents work safely against systems of record—never replacing a system's own authorization, only narrowing it. This is where most enterprise agent programs quietly fail.
Re-architect business processes to be AI-native. Sit with the people doing the work—finance, recruiting, sales operations, support, IT—to understand a workflow before designing for it, then partner with functional leaders to find the highest-leverage opportunities and ship them.
Set the standard for governance and safety. Capability boundaries, human approval for consequential actions, autonomy earned on evaluation evidence, audit trails, and lifecycle management—designed into the architecture rather than written into policy documents, and aligned with the security and compliance obligations we already carry.
Own the unit economics. Design for cost per completed task, not per token: model selection and routing, context management, caching, batching, and cost attribution teams can act on.
Make architectural decisions legible and durable. Write RFCs and ADRs, and keep interfaces stable so components can be swapped without re-architecting. Define the technical standards for AI development, deployment, and operation across the organization.
Multiply the team. Set the technical bar through architecture reviews and high-leverage code, mentor engineers across the US and India, and be the escalation point when a design decision crosses team boundaries.

Principal Engineer, AI/ML Security

Responsibilities

Qualification

Preferred

Benefits

Company

DigitalOcean

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