Asks for

The ad says“Agent orchestration — Experience building multi-step agentic workflows using tools like Claude Code, LangChain, or similar frameworks. Understands how to chain tools, handle failures gracefully, and design loops that don't go off the rails.”
The ad says“Agentic security posture — Understands the specific attack surface that comes with giving agents tool access: prompt injection, over-permissioned tool scopes, secrets leaking into context windows, and unintended data exfiltration through third-party API calls. Knows how to scope what an agent can see and do, apply least-privilege to MCP server access, and design systems where a compromised or misbehaving agent can't do catastrophic damage.”
The ad says“We're looking for someone with deep fluency in AI tooling and agentic systems, and enough full-stack engineering experience to build real things people depend on. You don't need to check every box — but here's the kind of background that sets you up well:”
The ad says“We're looking for someone with deep fluency in AI tooling and agentic systems, and enough full-stack engineering experience to build real things people depend on. You don't need to check every box — but here's the kind of background that sets you up well:”
The ad says“Cloud deployment & infrastructure basics — Knows enough to get things running on AWS, GCP, or Azure: containers, serverless functions, basic IAM, environment config. Doesn't need to be a DevOps engineer, but can't be blocked by "how do I deploy this."”
The ad says“Cloud deployment & infrastructure basics — Knows enough to get things running on AWS, GCP, or Azure: containers, serverless functions, basic IAM, environment config. Doesn't need to be a DevOps engineer, but can't be blocked by "how do I deploy this."”
The ad says“Cloud deployment & infrastructure basics — Knows enough to get things running on AWS, GCP, or Azure: containers, serverless functions, basic IAM, environment config. Doesn't need to be a DevOps engineer, but can't be blocked by "how do I deploy this."”
The ad says“Evaluations, metrics & observability — Can define what "working well" looks like before a tool ships and verify it stays that way in production. Writes evals and test sets for LLM behavior, instruments tools with usage metrics and logging, and builds enough observability that regressions surface before the team notices. Critically, knows when this rigor is actually warranted — not every internal tool needs a full eval harness, and good judgment about a tool's blast radius matters as much as knowing how to build one.”
The ad says“Lightweight full-stack / mini-app development — Can spin up a small web app or internal tool quickly. Doesn't need to be a frontend wizard, but should be comfortable enough to deploy something real that people actually use.”
The ad says“Cloud deployment & infrastructure basics — Knows enough to get things running on AWS, GCP, or Azure: containers, serverless functions, basic IAM, environment config. Doesn't need to be a DevOps engineer, but can't be blocked by "how do I deploy this."”
The ad says“Comfort with Python and a typed language — Go and/or TypeScript preferred, but what matters most is that you write code other people can read and maintain.”
The ad says“Knowledge base design & RAG — Can architect a retrieval system — chunking strategy, embedding models, vector stores, metadata design — so that agents and internal tools can query organizational knowledge usefully rather than just dumping docs into a pile.”
The ad says“Prompt engineering & LLM integration — Knows how to write effective system prompts, structure context windows, and integrate with LLM APIs (Anthropic, OpenAI, etc.). Not just "has used ChatGPT" — understands token limits, tool use, and how model behavior changes with prompt design.”
The ad says“MCP / tool & API integration — Comfortable wiring up internal and third-party MCP servers, REST/GraphQL APIs, and webhooks so agents can actually do things. The glue layer between LLMs and the rest of the world.”
The ad says“Prompt engineering & LLM integration — Knows how to write effective system prompts, structure context windows, and integrate with LLM APIs (Anthropic, OpenAI, etc.). Not just "has used ChatGPT" — understands token limits, tool use, and how model behavior changes with prompt design.”
The ad says“Comfort with Python and a typed language — Go and/or TypeScript preferred, but what matters most is that you write code other people can read and maintain.”
The ad says“Knowledge base design & RAG — Can architect a retrieval system — chunking strategy, embedding models, vector stores, metadata design — so that agents and internal tools can query organizational knowledge usefully rather than just dumping docs into a pile.”
The ad says“Comfort with Python and a typed language — Go and/or TypeScript preferred, but what matters most is that you write code other people can read and maintain.”

About Us

Integrate is building the first real-time, cross-boundary project management platform for the world’s most complex programs. We started with one of the most complex customers on earth, the U.S. government. Its programs span thousands of people, multiple organizations, billion-dollar budgets, and differentiated security environments. And somehow, too much of that work still runs on stale schedules, spreadsheets, hand-built slides, and project management software stuck in the Tetris era.

We’re changing that. Based in Seattle, Integrate has secured a $25 million U.S. Space Force contract and expanded across government and commercial programs, raised a $17 million Series A led by FPV Ventures, secured backing from J.P. Morgan, and built software that operates across government and commercial environments, from unclassified work to Top Secret networks.

We move fast, keep the egos low, and make room for quirks, strong opinions, fun, and the occasional chaos of building things from scratch with extreme velocity. We offer strong benefits, real ownership, and ambitious work with people you actually want to be around.

Our Solution

Integrate gives complex programs something they have never had before. One real-time schedule that can operate across organizations, security boundaries, and classification levels without fragmenting the schedule.

The program stays live as it changes. Teams can see how the program has moved, understand how a change ripples downstream, surface risk before it compounds, and control access down to the individual element. And our AI architecture works from that same continuously updated program state. It understands what is happening now, not a report assembled days or weeks ago. The schedule stops being a static plan and becomes a living reflection of the program itself.

We’re nowhere near done. We want to set a new standard for how the world’s most complex work gets done, and we won’t stop until we do.

About the Role

We're hiring an Agentic Operations Engineer to help us build the internal intelligence operating system of Integrate.

This role sits at the intersection of engineering, product, and operations. Your prime directive is to leverage modern AI tooling and agentic strategies to increase organizational velocity across engineering, product, design, and ops.

You are an engineer first. You've shipped real full-stack applications, and you understand the discipline of deploying and maintaining software people actually depend on. But what energizes you most is the next layer up: how do we structure a codebase, an organization, and a workflow so that humans and agents can collaborate fluidly? How do we eliminate the scavenger hunt for context and keep that context fresh? This is your purview.

This role is part tactical and part strategic. Tactically, you'll write code, build internal tools, automate pipelines and workflows, and audit repos so that the agents working alongside us route correctly and produce high-quality output. Strategically, you'll look ahead, parse requests coming in from product, engineering, design, and ops, and merge them into efforts that make sure we aren't doing redundant or parallel work across teams. You'll audit teams, unblock bottlenecks, stand up new systems, and ship tools that compound across the org.

You'll report directly to the VP of Product and work closely with engineering, product, and operations. This role is in person at our Ballard office in Seattle.

What You’ll Work On

Build the internal "intelligence OS" of Integrate — the tools, agents, automations, and conventions that compound the team's productivity over time
Organize our repos, docs, and conventions so that AI coding agents route correctly, find the right context, and produce high-quality output
Stand up and maintain internal tooling — chatbots employees use to find answers, CLI tools that support day-to-day workflows, small web apps that smooth over operational friction
Build and own automations around the PR review process — code quality checks, agent-driven first-pass review, security and convention enforcement
Partner with engineering to identify code-quality issues, ambiguous structures, or stale conventions that slow down both humans and agents, and propose fixes
Support the product team as they get closer to the codebase, and the design team as they work more directly in feature branches — pairing, building scaffolding, refining prompting workflows
Evaluate, deploy, and govern bleeding-edge AI tooling: IDE agents, evals, model gateways, MCP servers, on-prem systems
Audit teams proactively. Surface friction. Propose system-level fixes rather than one-off patches
Be the person teammates come to with questions, ideas, and half-built experiments — and the person who leads when no one is asking
Plan three to six months ahead for what an AI-native Integrate looks like, including on-prem and locally-run agent infrastructure for our most security-sensitive workflows
Contribute occasionally directly to the core application codebase when needed

Tech Stack

Our core application is built on React, Golang, GraphQL, REST, PostgreSQL, and GitHub Actions, with Tailwind CSS on the frontend. Familiarity with any of these is a plus, but you don't need to know our stack to be successful in this role. You'll spend most of your time in the layer above — building tooling, agents, and automations that work alongside our engineers, not necessarily inside our product.

Experience

We're looking for someone with deep fluency in AI tooling and agentic systems, and enough full-stack engineering experience to build real things people depend on. You don't need to check every box — but here's the kind of background that sets you up well:

AI & Agentic Systems

Prompt engineering & LLM integration — Knows how to write effective system prompts, structure context windows, and integrate with LLM APIs (Anthropic, OpenAI, etc.). Not just "has used ChatGPT" — understands token limits, tool use, and how model behavior changes with prompt design.
Agent orchestration — Experience building multi-step agentic workflows using tools like Claude Code, LangChain, or similar frameworks. Understands how to chain tools, handle failures gracefully, and design loops that don't go off the rails.
Agentic security posture — Understands the specific attack surface that comes with giving agents tool access: prompt injection, over-permissioned tool scopes, secrets leaking into context windows, and unintended data exfiltration through third-party API calls. Knows how to scope what an agent can see and do, apply least-privilege to MCP server access, and design systems where a compromised or misbehaving agent can't do catastrophic damage.
MCP / tool & API integration — Comfortable wiring up internal and third-party MCP servers, REST/GraphQL APIs, and webhooks so agents can actually do things. The glue layer between LLMs and the rest of the world.
Knowledge base design & RAG — Can architect a retrieval system — chunking strategy, embedding models, vector stores, metadata design — so that agents and internal tools can query organizational knowledge usefully rather than just dumping docs into a pile.
Evaluations, metrics & observability — Can define what "working well" looks like before a tool ships and verify it stays that way in production. Writes evals and test sets for LLM behavior, instruments tools with usage metrics and logging, and builds enough observability that regressions surface before the team notices. Critically, knows when this rigor is actually warranted — not every internal tool needs a full eval harness, and good judgment about a tool's blast radius matters as much as knowing how to build one.

Engineering & Tooling

Lightweight full-stack / mini-app development — Can spin up a small web app or internal tool quickly. Doesn't need to be a frontend wizard, but should be comfortable enough to deploy something real that people actually use.
Cloud deployment & infrastructure basics — Knows enough to get things running on AWS, GCP, or Azure: containers, serverless functions, basic IAM, environment config. Doesn't need to be a DevOps engineer, but can't be blocked by "how do I deploy this."
Developer experience & documentation mindset — Internal tooling lives or dies by adoption. Writes good READMEs, builds intuitive interfaces, and thinks about the engineers who will inherit or integrate with what they build.
Security fundamentals — Auth/authz, secret management, API surface design, and a working sense for what's safe to expose internally vs. externally.
Comfort with Python and a typed language — Go and/or TypeScript preferred, but what matters most is that you write code other people can read and maintain.

Approach

Comfort with ambiguity & rapid iteration — Internal tooling rarely has a tight spec. This person can talk to stakeholders, prototype fast, and figure out what the right thing to build actually is — often while building it.
3–5 years of engineering experience — Shipping real production software, not just tinkering. The years matter less than the evidence: things you've built, maintained, and handed off.
You don't need to be senior. If you spend your nights shipping side projects on the bleeding edge of AI tooling, that signal matters more than years on a resume.

Who You Are

You are a builder and a tinkerer. You can't help but try the new thing the day it ships. Your bookmarks tab is full of agent framework docs, half-finished MCP servers, and three different harness experiments. You ship.

You see organizational friction as a design problem, not a fact of life. When you notice the design team is hand-copying screenshots between tools, or engineers are re-explaining the same architecture decision for the fifth time, you don't shrug — you go build the fix.

You're a generalist in temperament. You're happy writing a CLI in the morning, evaluating a new model in the afternoon, and pairing with a designer on a feature branch in the evening. You enjoy supporting other people's work as much as your own, and you measure your impact by how much faster the team moves around you.

You are operationally rigorous. Integrate's customers build rockets, aircraft, and the systems that protect them. The tools you build are secure, observable, and don't fall over the moment someone else touches them.

You communicate openly, surface problems early, and don't wait for permission to fix things that are obviously broken.

Why Join Us?

Join us at the forefront of deep tech, building the software infrastructure that powers the next generation of hardware innovation. We’re a tight-knit, ambitious team with a vision to transform how humans build.

You’ll have the opportunity to shape the technical foundation of a company working with customers building rockets, aircraft, autonomous systems, and the infrastructure of the modern world.

ITAR Requirement

This position requires access to information protected under US export control laws, including the International Traffic in Arms Regulations and/or the Export Administration Regulations. As such, US person status (including US citizens, U.S. permanent residents, individuals granted U.S. asylum status, or individuals admitted in U.S. refugee status) is a required qualification for this position.

Equal Opportunity Employer

We are an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy), sexual orientation, gender identity and/or expression, national origin, protected veteran status, disability, genetics, or citizenship status (when otherwise legally authorized to work and access export-controlled data) and will not be discriminated against on the basis of such characteristics or any other status protected by the laws or regulations in the locations where we operate. We encourage applicants of all ages.