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.

AI infrastructure tooling

Build and maintain internal CLIs, SDKs, and services in Python or Go that let engineers provision, deploy, test, and debug AI agents across environments

Deploy, scale, and tune LLM inference using vLLM (and similar serving stacks), including GPU scheduling, autoscaling, batching, and cost/latency trade-offs

Build infrastructure for multi-agent systems: agent runtimes, tool and connector execution, sandboxing, state management, and safe rollout of new agent versions

Own MLOps workflows: model and prompt versioning, evaluation pipelines, artifact registries, and promotion from staging to production

Write Kubernetes operators and custom controllers (CRDs) that encode how Tessera's AI workloads are deployed and managed

Cloud platform and reliability

Design, build, and operate production-grade AI agent infrastructure on Kubernetes and Terraform

Support Tessera's portable infrastructure across Azure, Oracle Cloud, GCP, and AWS, with primary ownership of at least one cloud variant

Improve observability for AI systems: tracing of agent and LLM calls, token and GPU utilization, latency, error rates, and cost

AI Infrastructure Engineer [Tooling]

Responsibilities

Qualification

Required

Company

Tessera Labs

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