AI Architect

About the role

WhiteCrane builds and operates its own software, and we build it differently. Our engineers do not write most of the code. They direct fleets of AI agents that design, build, test, and deploy, and they own the architecture, the judgment, and the standard those agents are held to.

As an AI Architect, you sit at the center of that model: turning new ideas into production systems, and keeping the ones already live healthy and growing. This is a builder's role for someone excited that the job has changed, not nervous about it.

We are a small team running many products, fully remote and async. We add headcount reluctantly and tooling generously, which means unusual leverage here: the output of a team, in the hands of one person who knows how to direct it.

Expectations

• Own products end to end. Stand up new ones from an empty repository, and take the same ownership of the products already running in production.
• Support and evolve what is live. Maintain, harden, and extend existing services, and treat their reliability and uptime as yours.
• Set the technical direction. Make the architecture and tradeoff decisions, and define the patterns the rest of the work follows.
• Direct fleets of agents across the lifecycle. Plan, build, test, and release through orchestrated agents, reviewed to a production bar.
• Hold the quality bar. Establish the specs, reviews, and guardrails that keep agent-built software reliable, secure, and maintainable.
• Run it in production. Own cloud architecture, CI/CD, and observability, so what you ship stays healthy long after launch.

What we are looking for

We hire rarely, and the bar is high. We are looking for a rare generalist who could own a product end to end the way a founder would: excellent across the entire stack, not strong in one area and passing in the rest.

• 5+ years in the software industry developing cloud-native solutions. Enterprise experience with robust SDLC experience preferred.
• Deep in Python and/or .NET (C#), and confident dropping into any language, framework, or tool a problem calls for. You ask the right questions and plan before you build.
• Full-stack fluency. You build clean, responsive front ends in Angular or React / Next.js, and you are just as at home in the APIs and services behind them.
• Strong system design. You model data, shape APIs, and make sound architectural tradeoffs under ambiguity, and you know when the simplest solution is the right one.
• If it is not on the ticket, it did not happen. You document by habit in Jira or Azure DevOps, with a clear definition of ready and done, evidence of testing, and the PR linked.

Technical depth

The work spans the full stack and the full lifecycle: built with agents, shipped on Azure, and kept running in production. Here is where you should be strong.

Agentic fluency

• Context engineering. Treat context as the product, giving agents exactly what they need and nothing that distracts them.
• Retrieval and grounding. Build RAG pipelines over vector databases so agents work from real, current knowledge instead of guessing.
• Spec-driven development. Write precise, executable specifications, and drive agents from intent to implementation with frameworks like SpecKit or OpenSpec.
• Multi-agent orchestration. Design workflows where fleets of agents plan, build, test, and review, and know where a human has to stand in the loop.
• MCP and tooling. Build and wire MCP servers, giving agents safe, well-designed tools to act inside real systems.
• Evals and guardrails. Measure agent output, catch regressions early, and keep quality steady at speed.
• Taste and judgment. Know when to let an agent run and when to take the keyboard, and own the result either way.

Cloud and platform

• Azure, in depth. Architecture, identity, networking, and deployment across the services a product actually needs.
• Cost management. Design for efficiency and keep cloud spend honest, with the instincts to right-size as you scale.
• Release and DevOps. CI/CD with YAML pipelines, from build to gated production deploys.
• Authentication and authorization. OAuth 2.0, OIDC, role-based access, and disciplined secret management.
• Docker containerization. From local to production, with orchestration where it earns its keep.
• Third-party integrations. External APIs, webhooks, and SDKs, wired cleanly and kept resilient.
• AI and ML services. Real experience building with AI and ML services and models, well past a single API call.

Bonus experience

• Mobile. Native iOS with SwiftUI, or Android with Kotlin.
• Data engineering. Pipelines, warehousing, and moving data at scale.

Apply

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