Tessera compresses ERP modernization from years to weeks with governed, multi-agent AI. Backed by a16z.
About 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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Design and own CI/CD for infrastructure, services, and model/agent releases, using GitOps and pipeline-as-code
- Build self-service tooling that removes manual work: environment provisioning, secret rotation, dependency updates, compliance checks, and release management
- Partner closely with AI, product, and platform engineers to unblock delivery
- Champion an automation-first mindset: if something is done twice, it should be automated, if it's automated, it should be observable and maintained like production code
- 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
More about this role
Tessera Labs is redefining how enterprises adopt and operationalize Artificial Intelligence. Backed by Foundation Capital and led by a world-class founding team, we build multi-agent AI systems that automate complex business workflows across platforms like SAP, Salesforce, Workday, Snowflake, Databricks, and more.
Our mission is simple but ambitious: bring real AI automation to the enterprise — with speed, precision, and measurable impact. We move fast, operate with extreme ownership, and build at the frontier of applied AI.
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...
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