DevOps Engineer (Founding Team)
Location: San Francisco Bay Area
Type: Full-Time
Compensation: Competitive salary + meaningful equity (founding tier)
Backed by 8VC, we're building a world-class team to tackle one of the industry’s most critical infrastructure problems.
We're building an AI-native, multi-tenant enterprise platform for complex domains in industrial verticals. In this architecture, DevOps isn't just about shipping features — it's about operationalizing intelligent agents, ensuring traceability across AI systems, and supporting mission-critical ML infrastructure at scale.
We're looking for a DevOps engineer who can own infrastructure from Day 1 — automating everything from CI/CD and observability to cloud governance and security. You’ll work with a highly technical team building real-time AI pipelines and multi-agent systems. If you want to be the person who makes the platform run — fast, secure, reliable, and explainable — this is your role.
Build and maintain scalable cloud infrastructure across AWS/GCP/Azure with a focus on secure, tenant-isolated deployments
Own and evolve CI/CD systems (e.g. GitHub Actions, ArgoCD) with progressive rollout, testing, and rollback flows
Establish observability tooling across services, agents, and pipelines (OpenTelemetry, Prometheus, Grafana, Sentry)
Implement policy-as-code (OPA, Rego) for deployment safety, RBAC, audit logging, and approval workflows
Define and enforce SLAs, uptime targets (99.99%+), incident response, and remediation workflows
Secure infrastructure: IAM, VPC, encryption, key management, image scanning, secrets rotation
Automate deployments, infrastructure provisioning (Terraform, Helm), and environment replication
Core Experience:
4–10+ years in DevOps, platform engineering, or SRE in production-grade systems
Strong experience with Docker, Kubernetes (EKS/GKE), Terraform or Pulumi
Hands-on experience deploying and monitoring distributed cloud-native systems
Familiar with GitOps practices, CI/CD design, progressive delivery, and secure SDLC
Clear understanding of how to implement monitoring, alerting, and failure simulation in dynamic environments
Engineering Mindset:
Obsessed with reliability, latency, uptime, and repeatability
Security-aware and compliance-conscious
Proactive — you don’t wait for alerts to fix things
Comfortable collaborating with backend, AI, and data teams
Bonus: Agent-Native / ML Ops Capabilities
We’re building an agentic, AI-native platform from the ground up. Experience here isn’t required, but would be a strong differentiator:
Experience running LLM orchestration frameworks (e.g. LangChain, LangGraph, Dust, ReAct agents)
Building retrieval-augmented generation (RAG) pipelines — and deploying them safely and repeatably
Familiarity with vector DBs (Weaviate, Qdrant, Pinecone) and embedding pipelines
Monitoring and governing long-running or multi-agent chains
Auditability and replay systems for agent decision-making
Serving fine-tuned or open-source LLMs with model versioning and GPU scaling (e.g. vLLM, TGI)
Interest in auto-remediation using agents (e.g. observability + alert → insight → response via LLM)
DevOps is the nervous system of the platform — every agent, every data fabric component, every pipeline flows through what you build. This is a rare opportunity to design that system early, the right way, and future-proof it for scale, compliance, and trust.
If you're excited by intelligent systems, distributed data, and deeply technical infrastructure problems — and you want your work to have immediate real-world impact — we’d love to hear from you.
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