Staff AI Architect

DigitalOcean • Boston, MA

5h • $191,200 - $239,000 • Remote

About The Position

DigitalOcean is seeking an AI Architect to own the technical direction of their AI-native business. This role involves designing and building the agentic systems and enterprise architecture that enable any team within the company to deploy governed AI agents into business workflows quickly and safely. The architect will be responsible for the end-to-end architecture, including agent development, model integration, long-running work orchestration, and ensuring robust identity, audit, and observability. This is a hands-on role that requires writing code, building prototypes, and setting technical standards across distributed teams. The position offers a unique opportunity to partner with and influence the company's own AI platform and inference products.

Requirements

• Substantial experience as a software, solution, or enterprise architect (typically 10+ years), with experience owning architecture above a single project or team.
• Recent hands-on experience building LLM and agentic systems that ran in production, including agent orchestration (e.g., LangGraph, CrewAI, AutoGen), Model Context Protocol (MCP) tooling, retrieval and vector stores, and LLMOps discipline (evaluation-first development, prompt and agent versioning, regression testing, observability, cost attribution).
• Depth in at least one production language (Python, Go, TypeScript, or Java) and cloud-native infrastructure (Kubernetes, serverless, APIs, event-driven patterns, observability).
• Experience architecting on and integrating with enterprise systems like Workday, Salesforce, NetSuite, Greenhouse, or ServiceNow, understanding their data models, extensibility limits, native agent layers, and permissioning models.
• Experience with API and event-driven design, workflow and iPaaS platforms, and cloud data platforms, with a focus on how the integration layer must adapt for agentic workflows.
• Clear position on securing autonomous systems, including agents as first-class principals, short-lived machine identity, vault-backed scoped secrets, delegation with preserved provenance, default-deny tool access, prompt-injection defense, and audit trails.
• Experience with governance frameworks like NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
• Ability to translate ambiguous business problems into well-bounded AI systems.
• Excellent written and verbal communication skills, with the ability to influence without authority.
• Pragmatic decision-making with incomplete information and a bias for unblocking engineers.
• Effectiveness in distributed collaboration across time zones, including partnership with engineering teams in India, and comfort in a hybrid environment.

Nice To Haves

• Experience deploying AI developer tooling at scale to internal users (e.g., Cursor, Claude Code, GitHub Copilot).
• Experience re-engineering finance, people, GTM, or support processes in enterprise systems, including SOX-relevant or audited workflows.
• Familiarity with emerging agent interoperability and identity work (A2A, agent registries, SPIFFE/SPIRE, MCP authorization spec).
• Experience with agent evaluation and observability tooling (e.g., LangFuse, Arize, Braintrust, LangSmith, OpenTelemetry-based tracing).
• 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.

Responsibilities

• Own the AI reference architecture, defining patterns and standards for agentic systems including orchestration, tool use, capability boundaries, memory, retrieval, evaluation, and observability.
• Build, design, and prototype the most complex and ambiguous components of the AI platform, publishing reference implementations.
• Architect the internal AI platform, shaping shared services like model access, agent runtimes, evaluation harnesses, durable orchestration, and developer experience.
• Design the tool and capability layer, defining how agents discover and invoke capabilities using open standards like the Model Context Protocol.
• Design how agents integrate with enterprise systems, establishing patterns for integration, identity, and authorization.
• Re-architect business processes to be AI-native by collaborating with functional teams to understand workflows and identify high-leverage opportunities.
• Set the standard for governance and safety, embedding capability boundaries, human approval for consequential actions, autonomy based on evidence, audit trails, and lifecycle management into the architecture.
• Own the unit economics of AI workflows, designing for cost per completed task through model selection, context management, caching, batching, and cost attribution.
• Make architectural decisions legible and durable through RFCs and ADRs, defining technical standards for AI development, deployment, and operation.
• Multiply the team's impact by setting technical bars through architecture reviews, high-leverage code, mentoring engineers, and serving as an escalation point.
• Partner across the company with platform engineering, security, identity, data, and program management teams, and act as a technical advisor to business owners.

Benefits

• Competitive array of benefits to support well-being, varying based on local regulations and preferences.
• Employee Assistance Program.
• Local Employee Meetups.
• Flexible time off policy.
• Reimbursement for relevant conferences, training, and education.
• Access to LinkedIn Learning's 10,000+ courses.
• Bonus in addition to base salary (based on company and individual performance).
• Equity compensation, including equity grants upon hire.
• Option to participate in Employee Stock Purchase Program.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree