Staff AI Architect

Title: Staff AI Architect Location: Boston

We are looking for an Architect who will own the technical shape of how DigitalOcean operates as an AI-native business — the agentic systems our teams build, and the enterprise architecture they plug into.

DigitalOcean is building the substrate for an AI-native company, and this role owns its architecture. The ambition: any team—Finance, People, Sales, Marketing, Support, IT, Engineering—can put a governed agent into a real business workflow in hours or days, on rails that make the safe path the fast path. Credentials and access owned by the platform. Every agent operating under an identity that can be audited. Quality held to evaluation evidence as models and prompts change. Cost per workflow understood before it is committed. Orchestration and observability built once and shared. Getting there is an architecture problem, and it is yours.

Reporting to the Sr. Director of AI & Business Technology Engineering and partnering with the Manager, AI Engineering—who owns people and roadmap while you own technical direction—you will own the architecture end to end: how agents are built and run, how they reach models and enterprise systems, how long-running work is orchestrated, and how identity, audit, and observability run through all of it. You set the technical bar across our distributed team in the US and India.

The role is deliberately hybrid, and we mean all three parts. You are a software architect who sets the standard for how we build; an AI architect who has shipped agentic systems that survived production; and a business systems architect who knows agent quality is a context problem—and that the context lives in Workday, Salesforce, NetSuite, Greenhouse, Snowflake, and Okta, not in the model. Your influence spans all of Business Technology, not just AI Engineering.

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.

What You'll Do

• 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.
• Partner across the company. Serve as the architectural counterpart to our platform engineering, security, identity, data, and program management teams—and as technical advisor to business owners evaluating AI capabilities in their own platforms.

What Success Looks Like

• An architecture the whole team builds on, with interfaces durable enough that component and vendor swaps land without re-architecting.
• Provisioning a new agent is a single declarative step, governed by default—so the marginal cost of the next AI workflow collapses instead of compounding.
• Golden paths and reference implementations teams choose over hand-rolling their own.
• Agent behavior you can vouch for in production: strong eval scores against golden datasets, low regression and human-intervention rates, complete audit coverage, and fast time-to-detect and time-to-contain.
• Unit economics under architectural control: cost per completed task, and attribution accurate enough to bill back.
• Business outcomes on re-architected workflows—cycle time, hours returned, cost savings—and senior engineers leveling up because of your architecture and reviews.

What You'll Add to DigitalOcean

• Architecture Depth: Substantial experience as a software, solution, or enterprise architect (typically 10+ years), several of them owning architecture above a single project or team. A transformation you steered—what went wrong and what you changed—tells us more than a year count.
• Hands-On AI Engineering: Recent experience building LLM and agentic systems that ran in production: agent orchestration (LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI Agents SDK, or equivalents), Model Context Protocol (MCP) tooling, retrieval and vector stores, and LLMOps discipline—evaluation-first development, prompt and agent versioning, regression testing, observability for non-deterministic outputs, cost attribution.
• Production Engineering Fundamentals: Depth in at least one production language (Python, Go, TypeScript, or Java) and cloud-native infrastructure—Kubernetes, serverless, APIs, event-driven patterns, observability. You can open an editor and be useful on day one.
• Enterprise Systems Fluency: Experience architecting on and integrating Workday, Salesforce, NetSuite, Greenhouse, ServiceNow, or similar—their data models, extensibility limits, native agent layers, and permissioning models. You know when to extend a platform and when to build beside it.
• Integration Architecture: API and event-driven design, workflow and iPaaS platforms (Workato, MuleSoft, Boomi, n8n, or equivalents), and cloud data platforms—with a view on how the integration layer must change for agentic workflows.
• Agent Identity and Security Judgment: A clear position on securing autonomous systems: agents as first-class principals rather than credential-holders impersonating humans, short-lived machine identity, vault-backed scoped secrets, delegation with preserved provenance, default-deny tool access, prompt-injection defense, and audit trails your security team can use. Familiarity with the OWASP Agentic AI risk landscape.
• Governance Without Bureaucracy: Capability boundaries, an autonomy ladder promoted on evidence rather than anecdote, human-in-the-loop escalation, and data governance for prompts and outputs—plus fluency with the NIST AI RMF, ISO/IEC 42001, and the EU AI Act. You use them as tools, not as a shield.
• Business Translation: You can sit with a finance analyst or a recruiter, understand what they do all day, and turn an ambiguous business problem into a well-bounded AI system. Excellent written and verbal communication, and the ability to influence engineers, executives, and non-engineering stakeholders without authority.
• A Bias for Shipping: Pragmatic decisions with incomplete information, unblocking engineers rather than gating them, outcomes over outputs.
• Distributed Collaboration: Effective across time zones, including close partnership with engineering teams in India, and comfortable in a hybrid environment near our Boston/Cambridge community.

Bonus Points

• Experience deploying AI developer tooling at scale to internal users (Cursor, Claude Code, GitHub Copilot, or equivalent enterprise rollouts).
• Experience re-engineering finance, people, GTM, or support processes in enterprise systems, including SOX-relevant or otherwise audited workflows.
• Familiarity with emerging agent interoperability and identity work—A2A, agent registries, SPIFFE/SPIRE, and the MCP authorization spec.
• Agent evaluation and observability tooling (LangFuse, Arize, Braintrust, LangSmith, OpenTelemetry-based tracing, or equivalents).
• Knowledge graphs, semantic layers, or ontology modeling applied to enterprise retrieval, including GraphRAG patterns.
• Enterprise architecture practice experience (reference architectures, ADRs, C4 modeling, portfolio rationalization) or a framework background such as TOGAF—useful context, never a substitute for delivery.
• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Compensation Range: 

• $191,200- $239,000

*This is a remote role

JR: 2026-8013

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