Software Engineer - Agentic SDLC
Engineer on Optiver's Developer Experience team builds the Agentic SDLC: sandboxed per-task dev environments, trustworthy automated code review, multi-service test frameworks, hermetic build systems, and agent context layers. Stack includes Bazel/GitHub Actions, Kubernetes, and CD tooling like ArgoCD.
- Compensation
- $200K / yr
- Location
- New York, New York
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Built developer platforms or tooling used daily by a large engineering organisation
- Build and test infrastructure: build systems such as Bazel or Buck, and CI platforms such as GitHub Actions
- Development environment provisioning: systems such as Coder or GitPod, or built on Kubernetes directly
- Release and deployment systems: CD platforms such as ArgoCD or Spinnaker, and artifact repositories such as Artifactory
- Source control and code review: platforms such as GitHub or GitLab
- Shipped agentic systems that do real work inside the software development lifecycle (context design, tool integration, workflow orchestration, evaluation)
- Treat the platform as a product: instrument for adoption and friction, engage users regularly
- Build with security as a primary constraint: compute isolation, identity and access controls, network segregation
- Excited to work on unsolved problems; passionate about developer experience and how developers and agents work together
Full description
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organisations are treating AI as something that helps developers work faster - we think that framing may already be out of date.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.
What you’ll do
- 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.