AI Infrastructure Engineer [Tooling]

AI Infrastructure Engineer (Tooling) About Tessera Labs
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 Tesseras 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.
As an AI Infrastructure Engineer, youll design, build, and own the tooling and services that take Tesseras AI agents from a developers laptop to production in customer clouds. Youll write real software in Python or Go — CLIs, controllers, and internal services — and run it on Kubernetes alongside LLM serving stacks like vLLM.
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. 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
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 Tesseras AI workloads are deployed and managed
Cloud platform and reliability
Design, build, and operate production-grade AI agent infrastructure on Kubernetes and Terraform
Support Tesseras 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
Strengthen security and reliability for workloads running in customer environments under strict compliance constraints
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
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
~ Cloud security and DevSecOps: security gates, SBOM generation, and compliance checks in CI/CD
Deep networking expertise (service mesh, private connectivity, multi-cloud networking)
Open-source contributions to AI infrastructure or Kubernetes ecosystem projects
A builder who writes production-quality code, not just configuration
Curious about the fast-moving AI infrastructure landscape and eager to learn new tools
Someone who enjoys building foundational systems that other engineers rely on
Work on cutting-edge AI infrastructure with real enterprise impact
Shape how AI agents are deployed across the largest enterprise platforms in the world