AI-First SRE/DevOps Engineer

Axiad•San Jose, CA

1d•$120,000 - $160,000•Remote

About The Position

Axiad is seeking a skilled AI-First SRE/DevOps Engineer with 5–8 years of hands-on infrastructure and platform engineering experience to help build and run Mesh, our Identity Visibility and Intelligence Platform (IVIP) — a cloud-native microservices platform on Kubernetes spanning human identity, non-human identity (NHI), post-quantum cryptography, and agentic AI identity risk. The ideal candidate has a builder mentality and a strong AI-First mindset: automation and AI are the default, not the afterthought, and infrastructure is something you create, not just maintain. This is a startup environment. You will own real surface area end-to-end, move fast, and ship. The role requires deep operational expertise in Kubernetes, CI/CD, and infrastructure-as-code, along with practical experience running AI/LLM systems in production. If your instinct when facing a repetitive task is to script it, agent-ify it, or delete it entirely — you'll fit right in.

Requirements

5–8 years of professional experience in SRE, DevOps, or platform engineering roles.
Builder mentality: you'd rather create a tool, platform, or automation than run a manual process twice. You ship things and stand behind them.
Ownership: you take problems from ambiguity to resolution without waiting for a ticket, a spec, or permission. When something you own breaks, you're the first to know and the first to act.
Strong Kubernetes operational experience — running it in production, not just deploying to it.
Demonstrable adoption of an AI-First mindset and tools (Claude Code, Cursor, or Windsurf). Daily use of at least one AI development tool is a must.
Fluency with infrastructure-as-code, GitOps, and modern CI/CD; comfortable scripting and building tooling (Go or Python preferred).
Cloud-native depth on at least one major cloud provider.
Solid observability expertise and SLO-driven operations experience.
Experience with containerization (Docker) and service mesh concepts.
Strong problem-solving skills and a collaborative mindset; excellent communication within Agile teams.
A bias for shipping — startup pace energizes you rather than stresses you.

Nice To Haves

Experience building or operating LLM infrastructure: inference gateways, eval/observability tooling, agentic orchestration.
Data-pipeline and streaming/CDC experience.
Security or identity background; familiarity with post-quantum cryptography or supply-chain security.
Prior experience at an early-stage startup.

Responsibilities

Own reliability, observability, and delivery for a multi-tenant, cloud-native Kubernetes platform — from design through production, yours to run and yours to improve.
Build (not just operate) CI/CD pipelines, infrastructure-as-code, and GitOps-driven progressive delivery that let a small team ship many times a day, safely.
Embrace and advocate AI-First operations: automate incident response, runbooks, and remediation, and put AI agents in the loop to triage, diagnose, and propose fixes where it makes sense. Treat toil as a bug.
Build the infrastructure that AI-native features run on: inference gateways, LLM cost/latency observability, prompt/version pipelines, eval harnesses, and guardrails for agentic workloads.
Instrument everything — SLOs, error budgets, and distributed tracing across services and data pipelines.
Harden the platform: secrets management, supply-chain security, and least-privilege everywhere.
Troubleshoot and resolve production issues, leveraging AI-powered debugging and observability tooling.
Collaborate directly with product and platform engineers to translate requirements into resilient infrastructure — no throwing tickets over a wall; if you see a problem, it's yours to solve.
Mentor engineers in adopting AI-first operational practices and automation-by-default culture.