tessera-labs - AI Infrastructure Engineer [Tooling]
Requirements
• 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 • 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 • Proven experience deploying and operating real production systems • 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, 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 passion for clean, unified infrastructure and a builder mindset • A builder who writes production-quality code, not just configuration • Curious about the fast-moving AI infrastructure landscape and eager to learn new tools • Comfortable owning and defending infrastructure decisions, with clear reasoning about trade-offs • Experienced in startups and/or big tech environments • Highly self-motivated with a strong ownership mindset • Someone who enjoys building foundational systems that other engineers rely on
Responsibilities
• 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 • Strengthen security and reliability for workloads running in customer environments under strict compliance constraints
Benefits
• Work on cutting-edge AI infrastructure with real enterprise impact • Own foundational systems at an early-stage, high-growth company • Collaborate with a sharp, execution-focused founding team • Shape how AI agents are deployed across the largest enterprise platforms in the world
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