AI Infrastructure Engineer [Tooling]

Tessera Labs

23h•$200,000 - $230,000

About The Position

Tessera Labs is hiring an AI Infrastructure Engineer for its AI Platform team, which builds and operates foundational infrastructure for reliable, secure, and consistent operation of multi-agent AI systems across customer environments. The team works on portable cloud foundations and AI-specific infrastructure, including model serving, agent runtimes, evaluation and release tooling, and internal developer tools. In this hands-on individual-contributor role, you will design, build, and own tooling and services that move AI agents from development into production in customer clouds. You will write software in Python or Go, including CLIs, controllers, and internal services, and run it on Kubernetes alongside LLM serving stacks such as vLLM. The role is open at mid or senior level. Mid-level engineers own well-scoped components end to end; senior engineers additionally set technical direction, make platform-wide architectural decisions, and mentor others. Level will be calibrated based on experience and interviews. The position supports enterprise customer onboarding and operations under strict security and compliance constraints, while reducing integration risk, standardizing operations across cloud providers, and enabling product teams to ship AI features safely without infrastructure becoming a bottleneck. The role includes AI infrastructure tooling, cloud platform reliability, MLOps, CI/CD, automation, and developer experience. The company describes itself as an early-stage, high-growth business building multi-agent AI systems for enterprise workflows.

Requirements

At least 5 years of infrastructure, platform, or backend engineering experience; at least 7 years for senior level.
Strong software engineering skills in Python or Go, with a track record of building and shipping tools, CLIs, or services used by other engineers.
Hands-on experience running LLM workloads in production, including serving with vLLM or a comparable stack such as TGI, Triton, SGLang, or Ray Serve.
Experience building infrastructure for AI agents or LLM applications, including orchestration, tool calling, evaluation, or agent runtimes.
Practical MLOps experience with model or prompt versioning, evaluation and release pipelines, and ML-system observability.
Strong hands-on Kubernetes experience, including writing or extending operators or controllers using tools such as Kubebuilder, Operator SDK, controller-runtime, or Kopf.
Strong hands-on Terraform experience and experience with 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 production systems.
Production-quality software development skills and comfort owning and explaining infrastructure decisions and trade-offs.
Self-motivation and a strong ownership mindset.

Nice To Haves

GPU infrastructure experience with NVIDIA GPU Operator, MIG, KServe, or Karpenter-style autoscaling for inference.
Familiarity with agent frameworks and protocols such as LangGraph, Model Context Protocol, or OpenAI Agents SDK.
Experience with LLM observability tools such as OpenTelemetry, Langfuse, or Arize.
Experience with FluxCD, ArgoCD, or other GitOps controllers.
Cloud security and DevSecOps experience, including security gates, SBOM generation, and compliance checks in CI/CD.
Deep networking expertise, including service mesh, private connectivity, or multi-cloud networking.
Open-source contributions to AI infrastructure or Kubernetes ecosystem projects.
Experience in startup and/or big-tech environments.
A passion for clean, unified infrastructure and a builder mindset.
Curiosity about AI infrastructure and eagerness to learn new tools.
Enjoyment of building foundational systems that other engineers rely on.

Responsibilities

Build and maintain internal CLIs, SDKs, and services in Python or Go for provisioning, deploying, testing, and debugging AI agents across environments.
Deploy, scale, and tune LLM inference using vLLM or similar serving stacks, including GPU scheduling, autoscaling, batching, and cost/latency trade-offs.
Build infrastructure for multi-agent systems, including agent runtimes, tool and connector execution, sandboxing, state management, and safe rollouts of agent versions.
Own MLOps workflows, including model and prompt versioning, evaluation pipelines, artifact registries, and promotion from staging to production.
Write Kubernetes operators and custom controllers (CRDs) to define how AI workloads are deployed and managed.
Design, build, and operate production-grade AI agent infrastructure on Kubernetes and Terraform.
Support portable infrastructure across Azure, Oracle Cloud, GCP, and AWS, with primary ownership of at least one cloud variant.
Improve AI-system observability, including tracing agent and LLM calls and tracking token and GPU utilization, latency, errors, and costs.
Strengthen security and reliability for workloads in customer environments with strict compliance constraints.
Design and own CI/CD for infrastructure, services, and model or agent releases using GitOps and pipeline-as-code.
Build self-service tooling for environment provisioning, secret rotation, dependency updates, compliance checks, and release management.
Partner with AI, product, and platform engineers to unblock delivery.
Promote automation and ensure automated systems are observable and maintained like production code.
At senior level, set technical direction, make architectural decisions across the platform, and mentor others.