AI Infrastructure Engineer [Tooling]
Hands-on mid/senior IC role building the internal tooling and services (CLIs, controllers, SDKs in Python or Go) that let Tessera's multi-agent AI agents deploy and run reliably in customer clouds. Core stack: Kubernetes (operators), Terraform, vLLM LLM serving, multi-cloud, GitOps CI/CD, and MLOps pipelines.
- Compensation
- $200K–$230K / yr
- Location
- Remote in the U.S.
- Work setup
- Remote
- Seniority
- Middle
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Devops
- Posted
Skills
Must have
- 5+ years of experience in infrastructure, platform, or backend engineering (7+ for senior)
- Strong software engineering in Python or Go with a track record building tools, CLIs, or services other engineers rely on
- Hands-on experience running LLM workloads in production, including serving with vLLM or comparable stacks (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, observability for ML systems
- Strong hands-on Kubernetes experience, including writing or extending operators/controllers (Kubebuilder, Operator SDK, controller-runtime, Kopf)
- Strong hands-on experience with Terraform and at least one of Azure, Oracle Cloud, GCP, or AWS
- Experience designing and owning CI/CD systems, GitOps-driven deployments, and release automation
- Solid networking fundamentals
- Proven experience deploying and operating real production systems
Nice to have
- Experienced in startups and/or big tech environments
- GPU infrastructure experience: NVIDIA GPU Operator, MIG, KServe, or Karpenter-style autoscaling for inference
- Familiarity with agent frameworks and protocols (LangGraph, Model Context Protocol, OpenAI Agents SDK, or similar)
- LLM observability tooling such as OpenTelemetry, Langfuse, or Arize
- Experience with FluxCD, ArgoCD, or other GitOps controllers
- Cloud security and DevSecOps: security gates, SBOM generation, 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
Full description
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.
About 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.
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.
What You'll DoAI 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