Were looking for a System Architect to own the technical foundation of the Labs product end to end, from the whiteboard to production on Azure. Working closely with our Chief AI Officer, youll translate complex research assumptions and decisioning methodologies into clean, self-service products. You will port the right pieces of our engine into a lean new stack, make it integrate cleanly across the business environments our customers already run, and lead the coding yourself. This is a hands-on architect role, not an oversight role: you set the design and youre in the codebase every day.
A core part of the mandate is a fully agentic SDLC, building the product with AI coding agents end to end, not just building an agentic product. We expect a small team to ship like a large one by making agent-driven development the default way we design, write, and review code. Youll define how we do that.
What youll do:
Translate methodology into product. Partner with the Chief AI Officer to turn complex assumptions, math, and decisioning methodologies into clean, self-service products a customer can run without a data-science team.
Own the architecture. Design a scalable, cloud-native system on Azure, covering services, data flow, model-serving and the end-to-end decision pipeline, and keep it coherent as it grows.
Lead the coding. Set the standards, make the hard build-vs-buy calls, and write the core code yourself. Youre the technical center of gravity for the team. You will guide and work shoulder-to-shoulder with the other engineers through the architecture and the code, without managing them directly. Leadership here is by design and example, not by org chart.
Stand up a fully agentic SDLC. Establish how we design, generate, review, and ship code with AI agents end to end: the tooling, guardrails and CI gates that let a small team punch far above its size.
Stand up the environment. Establish a fresh, greenfield Azure environment for Labs on a Kubernetes-native, Everything-as-Code foundation: IaC (Terraform, Helm), GitOps (ArgoCD or similar) and CI/CD from day one. In the early days that means rolling up your sleeves on the platform groundwork (subscriptions, networking, environments), working hands-on alongside Microsofts teams to get us live.
Requirements: 10+ years in software engineering with vast, hands-on production experience. You have architected, shipped, and run large-scale cloud-native systems in the real world, and that scar tissue is what lets us move fast in the right direction and avoid expensive wrong turns.
Hands-on and current: you architect and code. Strong in Python (our primary language; Go/Bash a plus) and comfortable owning a service from design to production.
Kubernetes-native by instinct (declarative-first, controllers and operators, Docker) with real Everything-as-Code discipline: IaC (Terraform, Helm), GitOps (ArgoCD or similar), and a defined, CI-gated SDLC.
Production-first: you design for reliability, observability, and recoverability, with clear SLOs, sound alerting and blameless postmortems, and you own what you ship.
Real, hands-on Azure depth at production scale. Labs runs entirely on Azure and we are standing the environment up from zero, so this is a hard requirement, not a nice-to-have. You need to know Azure already rather than learn it here.
Experience designing systems that run ML / data-science workloads in production (model serving, feature/data pipelines, inference at scale).
A data-engineering and data-science foundation is a big plus. You are comfortable with data pipelines, feature engineering, and reasoning about data quality, and you can speak the language of the data scientists youll build for.
This position is open to all candidates.