Top Artificial Intelligence DevOps & Platform Engineering Jobs

Operate and improve large-scale AI compute, storage, and networking infrastructure in 24x7 environments. Responsibilities include capacity planning, performance analysis, patching, upgrades, firmware management, automation, incident response, documentation, disaster recovery testing, and L3 escalation support. The role also requires customer collaboration, operations enablement, and participation in an on-call rotation while supporting OpenStack or similar private cloud platforms.

Build, standardize, and operate compute and software infrastructure for AI and HPC validation labs. Responsibilities include cluster provisioning, Linux images, drivers, Kubernetes or Slurm orchestration, benchmark harnesses, workload libraries, observability, dashboards, incident response, root-cause analysis, and automation. The role partners with architecture and performance teams to mirror customer deployments and support repeatable validation across AI, networking, memory, and DPU use cases.

Owns deployment, lifecycle management, and evolution of a multi-prem platform across cloud, bare-metal, air-gapped, and regulated environments. Builds infrastructure-as-code and secure CI/CD pipelines, maintains Kubernetes clusters and observability systems, aligns SaaS and customer-hosted releases, and supports incident response and upgrades. The role also contributes to platform architecture, deployment documentation, compliance workflows, and secure operations across classified and government environments.

Design, deploy, and maintain resilient AWS and Kubernetes infrastructure. Build automation, internal tools, CI/CD components, and AI-assisted operational workflows. Establish observability, SLIs, SLOs, and incident-management practices while leading postmortems and reducing toil. Ensure HIPAA compliance, review architecture, identify risks, and mentor engineers on reliability and platform best practices.

Leads the architecture and development of scalable, secure infrastructure and developer platforms supporting healthcare AI products. Responsibilities include cloud systems, automation, observability, CI/CD, Kubernetes, infrastructure as code, security, reliability, technical strategy, cross-team initiatives, mentorship, and periodic on-call support.

Own and improve Wispr’s developer experience systems, including CI/CD pipelines, reproducible testing environments, local development workflows, debugging tools, and agentic coding infrastructure. The role focuses on accelerating feedback, improving reliability, reducing engineering friction, and partnering with engineers to drive adoption and ongoing operational excellence across client and backend codebases.

Owns production infrastructure across Kubernetes, CI/CD, Linux systems, virtual machines, and AI accelerator environments. Responsibilities include incident response, troubleshooting, automation, monitoring, alerting, patching, upgrades, certificate renewals, capacity checks, runbook development, and on-call support. The engineer collaborates across IT and product software teams and contributes to infrastructure design and build work.

Designs and supports enterprise DevOps tooling, including Jenkins, Bitbucket, and JFrog Artifactory. Responsibilities include disaster recovery, repository and artifact governance, open-source dependency security, DevSecOps controls, technical documentation, and platform migration support. The role also promotes AI tool adoption, provides project oversight, mentors colleagues, and works collaboratively with clients and engineering teams.

Build and maintain a scalable AI travel management platform. Responsibilities include designing observability, monitoring, alerting, and reliability systems; managing and upgrading AWS infrastructure across environments; developing concurrent software; designing scalable database models; improving deployment speed; and contributing to backend architecture, security, testing, documentation, and software engineering standards.

Work directly with customers to own implementations end-to-end: discovery, integration, deployment, optimization. Write production code, build automations from messy workflows, and serve as the technical face of the company to ensure customer success.