Job description

WHO WE ARE Percepta’s mission is to transform critical institutions with applied AI. We care that industries that power the world (e.g. healthcare, manufacturing, energy) benefit from frontier technology. To make that happen, we embed with industry-leading customers to drive AI transformation. We bring together: challenge.percepta.ai - Forward-deployed expertise in engineering, product, and research - Mosaic, our in-house toolkit for rapidly deploying agentic workflows - Strategic partnerships with Anthropic, McKinsey, AWS, companies within the General Catalyst portfolio, and more Our team is a quickly growing group of Applied AI Engineers, Embedded Product Managers and Researchers motivated by diffusing the promise of AI into improvements we can feel in our day to day lives. Percepta is a direct partnership with General Catalyst, a global transformation and investment company. ABOUT THE ROLE 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. WHAT YOU'LL DO - 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. - Make HIPAA and SOC 2 properties of the infrastructure rather than projects: controls in code, evidence generated by the pipeline. - Own the observability stack we already run — Grafana, Loki, Mimir, Tempo, Alloy, Langfuse, LiteLLM — and make it answer operational questions about agent behavior: which agent decided what, on whose data, at what cost. - Give the engineers who ship into these tenants a paved road, so deploying doesn't require knowing our blueprint template engine. WHAT WE'RE LOOKING FOR - 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. NICE TO HAVE - 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. ABOUT OUR TEAM As an organization, Percepta is committed to inclusion and belonging, and our people are our greatest investment! We’re working against an incredibly ambitious mission. It won’t be easy, but it will likely be the most fulfilling work of your career. In addition to competitive compensation, equity, 401(k) matching, comprehensive health and family benefits, and lunch every day, we offer a culture built on autonomy and trust. We strongly believe that the boldest solutions come from teams who dream bigger together, and that means bringing in people from every kind of background, experience, and path. We are an office-first team that values the impact of in-person collaboration, but we focus on outcomes over outputs—believing that we are each responsible for solving the "whole problem" to make our customers win. If you don't check every box on this description but you care deeply about the problems we're tackling, seeking the truth, and wanting to own outcomes alongside a team that has your back, then we want to hear from you! We know self-doubt keeps great people from applying, so please don't count yourself out before we've had the chance to meet you. Tackling the hardest problems takes a range of perspectives in the room, and we believe that's how we build something truly game-changing. Our company values are more than just words on a page. They inform how our team shows up every day: OUR VALUES Dream bigger: We have the unique privilege of taking on the most ambitious problems and we should chase them with optimism, responsibility, and genuine belief that we can make it happen. We have to embrace the hard things when no one else will. Heart in the game: What we're doing matters and we have to give a shit. Internally, that means fixing badness when you find it. Externally, it means honoring the trust our customers place in us with their most important problems. This isn’t a 9-5, nor is it a job we’re ever going to monitor your hours. We promise to put work in front of you that matters and in return, we ask you to promise to care. Win for the customer: Everyone is an engineer and the job of an engineer is to deliver outcomes, not outputs. Everything we do—the products we build, the partnerships we launch, the strategy we set—exists to make our customers successful. Delivery is the strategy. Make the call: Organizations are only as strong as the pace at which they make decisions. Everyone at Percepta should feel empowered to commit and shape the ambiguity in front of them. But "make the call" cuts both ways: make the decision and make the phone call. High-agency decision-making only works with high-bandwidth communication and we commit to never operate in silos. Intensity with kindness: We believe in excellence in execution, candor in feedback, ruthlessness in prioritization, and survivalist urgency. We also believe you don't need to be an asshole to deliver on any of this. The trust built through shared kindness and vulnerability is what makes the intensity sustainable. Percepta is proud to be an equal opportunity employer and we evaluate every qualified applicant on their merits and give equal consideration for employment regardless of race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, veteran or uniformed service status, age, or any other characteristic protected under applicable law. Percepta provides reasonable accommodations to qualified applicants and employees in accordance with federal and applicable state law.

Requirements

Must-haves

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.
The infrastructure patterns for the agentic systems of the future don't exist yet. You'll help define them.
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.
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.
Make HIPAA and SOC 2 properties of the infrastructure rather than projects: controls in code, evidence generated by the pipeline.
Own the observability stack we already run — Grafana, Loki, Mimir, Tempo, Alloy, Langfuse, LiteLLM — and make it answer operational questions about agent behavior: which agent decided what, on whose data, at what cost.
Give the engineers who ship into these tenants a paved road, so deploying doesn't require knowing our blueprint template engine.
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.
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.

Nice-to-haves

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.
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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsazuregcpkubernetespythongollm

Platform: ashby
Effort: quick

Benefits

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401(K)