AI Infrastructure Engineer [Tooling]
Tessera Labs, Remote in the U.S.
The role
As an AI Infrastructure Engineer, you'll design, build, and own the tooling and services that take Tessera's AI agents from a developer's laptop to production in customer clouds. You'll write real software in Python or Go — CLIs, controllers, and internal services — and run it on Kubernetes alongside LLM serving stacks like vLLM.
This is a hands-on IC role open at mid to senior level. Mid-level engineers will own well-scoped components end to end; senior engineers will also set technical direction, make architectural calls across the platform, and mentor others. We'll calibrate level based on experience and interviews.
The team
The AI Platform team builds and operates the foundational infrastructure that lets Tessera's multi-agent systems run reliably, securely, and consistently across customer environments. That spans the portable cloud foundation (Kubernetes, networking, automation) and the AI-specific layer on top of it: model serving, agent runtimes, evaluation and release tooling, and the internal CLIs and services our engineers use every day.
The work directly enables us to onboard and operate enterprise customers under strict security and compliance constraints while preserving developer velocity. It reduces integration risk, standardizes operations across hyperscalers, and makes sure product teams can ship AI features safely without infrastructure becoming a bottleneck.
What you need
Must-have qualifications
- 5+ years of experience in infrastructure, platform, or backend engineering (7+ years for senior)
- AI infrastructure tooling: strong software engineering in Python or Go, with a track record of building and shipping tools, CLIs, or services that other engineers rely on
- Hands-on experience running LLM workloads in production, including serving with vLLM or a comparable stack (TGI, Triton, SGLang, Ray Serve)
- Experience building infrastructure for AI agents or LLM applications: orchestration, tool calling, evaluation, or agent runtimes
- Practical MLOps experience: model/prompt versioning, evaluation and release pipelines, and observability for ML systems
- Strong hands-on experience with Kubernetes, including writing or extending operators/controllers (Kubebuilder, Operator SDK, controller-runtime, or Kopf)
- Strong hands-on experience with Terraform and at least one of Azure, Oracle Cloud, GCP, or AWS
- Proven experience designing and owning CI/CD systems, GitOps-driven deployments, and release automation
- Solid networking fundamentals
- Proven experience deploying and operating real production systems