Software Engineer - Agentic SDLC

At Optiver, the velocity of our software development is a critical part of our competitive advantage. Our ability to compete depends on how quickly new ideas can move from insight to production. The Developer Experience team is a strategic engineering function with a clear mandate: improve the software development lifecycle across the company in ways that accelerate delivery and reduce our time to market. We build the tools, systems, and platforms that shape how software is developed at Optiver.

Many organisations are treating AI as something that helps developers work faster - we think that framing may already be out of date. Agents are now capable of doing work end-to-end, not just assisting with it - at a volume and cadence our current software development lifecycle was never designed to absorb. That shifts the problem we are trying to solve from how to make a developer faster to how to make an agent capable of running the entire lifecycle on its own both safely and efficiently.

As it turns out, this is more of a platform problem than a model problem. An agent is only as autonomous as the least autonomous step around it: the environment it needs and cannot provision, the credentials it has no identity to hold, the integration test it cannot stand up, the review that still waits on a human. Such gaps were invisible while everything ran at a human pace but are now what set the ceiling.

We’re looking for engineers who are excited by this problem and who want to help build the Agentic SDLC at Optiver. As part of our Developer Experience team, you’ll lead the development of the platforms, workflows, and guardrails that make autonomous development not only possible but also secure, measurable, and effective at scale.

• Build just-in-time development environments for agents. Sandboxed compute provisioned per task rather than assigned to a person; sized and configured for the work in front of it, ready in seconds and torn down after.

• Make automated code review trustworthy. Review that weighs the whole case for a change — spec, tests, implementation decisions — not just the diff, and decides whether it is safe to ship, whether a human needs to be involved, and what to put in front of them.

• Develop testing frameworks that build confidence in agentic output. Complex, multi-service tests running in realistic environments against real dependencies, proving with evidence what any given change does to system behaviour.

• Establish a chain of custody for every change. An end-to-end trace of where a change came from, what agents acted on it under which credentials, and how it was built — complete enough to verify what it carries and prevent anything insecure from reaching privileged production environments.

• Build evaluation into the platform. Every model upgrade, context change and harness tweak alters how agents behave — measurement allows us to tune the platform on evidence rather than intuition and ensure that the quality of output is improving over time.

• Design build systems that hold up under an order of magnitude more change. Hermetic, incremental builds backed by remote execution and distributed caching — fast enough that agents aren't left waiting, reliable enough that a failure always means something, on capacity that scales with demand.

• Build the context layer agents navigate by. Connecting source, documentation, build and deployment systems and operational signals, so an agent knows which repo owns which part of the system, where a service is deployed and how to build it — enough to take a spec and turn it into a real change on its own.

• Work directly with engineering teams. Understand how they actually build, find