Scan your resume against ATS criteria for this Staff AI Architect role at DigitalOcean.
Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world.
DigitalOcean is building the substrate for an AI-native company, and this role owns its architecture
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
who sets the standard for how we build; an
who has shipped agentic systems that survived production; and a
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
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.
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 to take. Standards enforced through tooling and paved paths, not approval boards.
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.
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.
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.
Establish the integration, identity, and authorization patterns that let agents work safely against systems of record—never replacing a system's own authorization, narrowing it. This is where most enterprise agent programs quietly fail.
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.
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.
Design for cost per completed task, not per token: model selection and routing, context management, caching, batching, and cost attribution teams can act on.
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.
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.
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.
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.
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 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.
*This is a remote role
JR: 2026-8013
#LI-Remote
You’ll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.
Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
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 to take. Standards enforced through tooling and paved paths, not approval boards.
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.
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.
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.
Establish the integration, identity, and authorization patterns that let agents work safely against systems of record—never replacing a system's own authorization, narrowing it. This is where most enterprise agent programs quietly fail.
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.
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.
Design for cost per completed task, not per token: model selection and routing, context management, caching, batching, and cost attribution teams can act on.
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.
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.
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.
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.
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 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.
*This is a remote role
JR: 2026-8013
#LI-Remote
You’ll be a part of a cutting-edge technology company with an upward trajectory, who are proud to simplify cloud and AI so builders can spend more time creating software that changes the world. As a member of the team, you will be a Shark who thinks big, bold, and scrappy, like an owner with a bias for action and a powerful sense of responsibility for customers, products, employees, and decisions.
At DO, you’ll do the best work of your career. You will work with some of the smartest and most interesting people in the industry. We are a high-performance organization that will always challenge you to think big. Our organizational development team will provide you with resources to ensure you keep growing. We provide employees with reimbursement for relevant conferences, training, and education. All employees have access to LinkedIn Learning's 10,000+ courses to support their continued growth and development.
Regardless of your location, we will provide you with a competitive array of benefits to support you from our Employee Assistance Program to Local Employee Meetups to flexible time off policy, to name a few. While the philosophy around our benefits is the same worldwide, specific benefits may vary based on local regulations and preferences.
The salary range for this position is based on market data, relevant years of experience, and skills. You may qualify for a bonus in addition to base salary; bonus amounts are determined based on company and individual performance. We also provide equity compensation to eligible employees, including equity grants upon hire and the option to participate in our Employee Stock Purchase Program.
We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.
How to apply for Staff AI Architect at DigitalOcean?
Click the "Apply on Company Website" button on this page to submit your application directly on the employer's official portal.
What is the salary for this role?
Salary details will be discussed during the interview.
What experience is required?
10+ yrs of experience is required.
Is this position still open?
Yes, currently active and accepting applications.
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