AI INFRASTRUCTURE ENGINEER — AGENTIC AI PLATFORM

## About the Role AI platforms have infrastructure requirements that general-purpose cloud deployments do not — low-latency inference pipelines, privacy-preserving data flows, and the ability to shift computation between cloud and edge as the platform evolves alongside wearable hardware. As a Lead Infrastructure Engineer you will own the systems that make all of this possible. You will work directly with the CEO and across the full engineering team — backend, mobile, and AI — to build and operate the infrastructure layer that keeps the platform fast, reliable, secure, and scalable as we grow from a closed beta to a production consumer product. This is a hands-on role. You will design, build, and operate the infrastructure yourself — not manage a team of engineers doing it. You will be the person the engineering team relies on when deployment pipelines break, latency spikes, or a new AI workload needs to be provisioned correctly. You will also be the person who builds the systems that prevent those problems from happening in the first place. We actively use AI-assisted development tools across our engineering team — Cursor, Claude, Copilot — and expect engineers who use them seriously as a core part of their workflow. ## What You'll Build and Own - CI/CD pipelines — the automated build, test, and deployment infrastructure that lets a distributed engineering team across Silicon Valley, Paris, and Shenzhen ship confidently and quickly - AI workload infrastructure — the compute, networking, and storage configurations that support LLM inference, embedding generation, vector search, and RAG pipelines at low latency and meaningful scale - Kubernetes cluster management — provisioning, scaling, and operating containerized services across cloud environments, with particular attention to the cost and latency tradeoffs of AI workloads - Observability and monitoring — the logging, metrics, alerting, and tracing systems that give the engineering team full visibility into platform behavior in production - Security and compliance infrastructure — the systems that enforce our privacy-by-design architecture, including encrypted data pipelines, secrets management, network security, and access controls - Infrastructure as code — Terraform, Helm, ArgoCD or equivalent, ensuring the entire infrastructure is reproducible, version-controlled, and auditable - Edge and on-device infrastructure planning — as the platform transitions from cloud-first to edge-first over the next 12 to 18 months, you will be the person who designs the infrastructure architecture that supports that transition ## What We're Looking For - 4+ years of infrastructure or DevOps engineering experience, with at least 2 years working on AI or ML platform infrastructure specifically - Strong Kubernetes experience — you have operated Kubernetes clusters in production and understand the tradeoffs of different configurations for AI workloads - Hands-on experience with major cloud providers — GCP and AWS are our primary candidates and experience with either is directly relevant - Infrastructure as code proficiency — Terraform is the standard; Helm and ArgoCD experience is a strong plus - Genuine understanding of AI infrastructure requirements — you know what makes LLM inference pipelines different from standard web services and have made infrastructure decisions with those differences in mind - Experience with vector databases and embedding infrastructure — Pinecone, Weaviate, pgvector, or similar - Strong observability experience — you have built monitoring and alerting systems that surface real problems without generating noise - Security and privacy infrastructure experience — you understand how to build systems that enforce data privacy at the infrastructure layer, not just the application layer - Active user of AI-assisted development tools with a genuine point of view on how to use them well - Strong written English and proven ability to work effectively in a remote and distributed team - Must be authorized to work in the US - Strong plus: experience with edge or on-device inference infrastructure — managing the transition of compute from cloud to device; multi-region deployment experience relevant to our globally distributed team and regionally diverse LLM routing architecture; experience with encrypted data pipelines and privacy-preserving infrastructure ## What We Offer - Salary: competitive depending on experience - Meaningful early-stage equity - Full medical, dental, and vision coverage - Fully remote with occasional in-person time in Silicon Valley or Europe for key milestones - Awear is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.