Responsibilities

We're hiring an AI Infrastructure Engineer to own the infrastructure, deployment, and operational reliability that powers Percepta's AI systems, including the autonomous agents at the core of what we ship.
Part of the work is hardening what exists: tightening our Terraform footprint, strengthening deployment pipelines, bringing more rigor to how we manage infrastructure across regions and providers. Part of it is building what's missing. And part of it is genuinely new territory, figuring out what SRE means when the systems you're operating make autonomous decisions.
The infrastructure patterns for the agentic systems of the future don't exist yet. You'll help define them.
Why this is different
You're deploying autonomous systems. The infrastructure contract changes when your workloads have agency.
Observability means understanding why an agent made a decision, not just whether a pod is healthy.
The gap between research and production is real here. Our teams move optimization algorithms and AI systems from research environments into production, and you'll be part of that handoff. MLOps experience isn't required, but you'll be closer to that boundary than most infra roles.
Small team. Real ownership. You're making foundational decisions, not inheriting someone else's.
Build the operational floor: SLOs, alert routing that actually reaches a human, an on-call rotation, incident response, and postmortems that produce changes. The alerts exist; the paging doesn't.
Own and evolve the IaC - Terraform across AWS, Azure, and GCP, plus the blueprints and per-environment installations that drive it. Bring it tests, policy-as-code, and drift discipline.
Make standing up a new customer environment boring: automate the firewall exceptions, DNS delegations, deploy identities, tag policies, and cert chains that are a runbook today.
Keep the three clouds at parity. A service that exists on Azure but not GCP is a half-shipped service, and we don't get to pick which cloud a customer already bought.

Qualification

Deep Terraform - module designComfortable customer-facingPythonMulti-region experienceReal depth in the Grafana stack — MimirGPU and inference operations

Required

5+ years operating production infrastructure - SRE, platform, or production engineering.
You've operated software in environments you don't control: BYOC, single-tenant, on-prem, or air-gapped. You know what changes when you can't just open the console.
Cloud-agnostic by instinct and comfortable in all three of AWS, Azure, and GCP - networking, IAM, managed Kubernetes (EKS, AKS, GKE), and the operational differences that actually bite. Deep in one is table stakes; we need someone who treats the other two as first-class, not as ports.
Kubernetes in production on managed clusters across multiple providers, and the judgment to know when a cloud-native service is the right call versus a portable one.
Deep Terraform - module design, state, testing, drift, and the judgment to know which blast radius is acceptable.
You've carried a pager and then built the thing that made it quieter: incident command, postmortems, SLOs that changed someone's behavior.
You've worked under a regulated posture — HIPAA, SOC 2, PCI, FedRAMP — where controls had to live in the infrastructure, not a spreadsheet.
Comfortable customer-facing. You will sit in a customer's architecture review and ask their network team for a firewall exception.
Python, Go, or Bash, and the instinct to automate a process the second time you do it.
Genuine curiosity about what you're operating. Agents, not just pods.

Preferred

You've operated infrastructure through a vendor control plane (Ryvn, Nuon, Replicated, or similar) and have opinions about the tradeoff.
GitOps and progressive delivery across a fleet of tenants running different versions.
Multi-region experience, and a view on where provider-native beats portable in a BYOC product.
Real depth in the Grafana stack — Mimir, Loki, and Tempo at multi-tenant scale.
GPU and inference operations: Ray, SkyPilot, vLLM or SGLang, LLM gateways and cost attribution.
MLOps or research-to-production handoff experience. Not required — you'll be near that boundary either way.
You've thought about what observability means for non-deterministic systems, and what a blast-radius control looks like for something that acts on its own.
The infrastructure patterns for autonomous AI systems are still being written. If you want to be one of the people writing them, let's talk.

AI Infrastructure Engineer at Percepta · LumiJobs

AI Infrastructure Engineer

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Percepta

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