Top Artificial Intelligence DevOps & Platform Engineering Jobs

Build fleet health, repair and RMA workflows, hardware qualification systems, facility maintenance and asset-management platforms, and structured operational procedures for large AI compute deployments. The role involves production software development, LLM and agent integrations, Kubernetes and bare-metal operations, hardware validation, incident reduction, and forward-deployed collaboration with production engineers and facility operators.

Own deployment, provisioning, CI/CD, containerization, security, and lifecycle management for software running on autonomous UGVs and edge devices. Build reproducible deployment infrastructure across NVIDIA Jetson platforms, embedded Linux systems, fleets, and cloud environments. Collaborate with embedded, autonomy, and systems teams on board bring-up, production updates, secure boot, configuration management, and intermittently connected or air-gapped deployments.

Lead the Platforms DevOps team managing Kubernetes clusters and cloudops infrastructure. Drive automation, cost optimization, security improvements, cross-team collaboration, and team development while overseeing CI/CD, incident response, and production reliability.

Lead platform infrastructure strategy and build reusable service infrastructure across Kubernetes, AWS, networking, and operational systems. Develop Helm charts, Terraform modules, CI/CD, GitOps workflows, zero-trust policies, and self-service platform capabilities. Partner with service, SRE, security, networking, and infrastructure teams to modernize services and drive adoption of shared patterns. Lead cross-company infrastructure initiatives and establish an initial platform infrastructure team.

Build and operate shared platform foundations for a multi-tenant product, including secure APIs, identity and authorization, tenant isolation, workflow orchestration integrations, reusable services, observability, usage metering, and production adoption. Partner with product, data, power systems, and SRE teams; make architecture and implementation decisions; improve reliability; and mentor engineers.

Customer-facing technical engineer who leads PoCs/PoVs, implements and validates deployments, troubleshoots issues, builds representative environments for release validation, and captures feedback to improve product, delivery, and post-sales processes.

Lead platform engineering across infrastructure, CI/CD, testing, developer tooling, observability, and secure software delivery. Build internal platforms, golden paths, and shared tooling that improve developer and AI-agent productivity. Establish engineering standards, integrate security controls into pipelines, shape repositories for coding agents, and measure impact through DORA metrics and engineering feedback.

Lead the modernization of engineering tooling environments by migrating legacy platforms to Azure-based cloud infrastructure. Deploy and support CI/CD, artifact management, code quality, and licensing tools; develop Infrastructure as Code and automation; maintain existing environments; and provision infrastructure resources. Implement monitoring, backups, archiving, and logging best practices while supporting engineering users. Collaborate with product and delivery stakeholders, coach junior team members, and champion reliable, secure DevOps practices.

Administers and optimizes high-performance computing infrastructure, including Linux and Windows systems, compute nodes, parallel storage, InfiniBand networks, Slurm scheduling, backups, virtualization, and monitoring. Supports system imaging, patching, security, disaster recovery, capacity planning, software licensing, and infrastructure upgrades. Collaborates with enterprise IT, information security, vendors, faculty, and students; documents procedures, provides technical guidance, participates in projects, and performs on-call support. Requires hands-on troubleshooting, automation, customer service, and continuous infrastructure improvement.

Operate and scale Kong’s global multi-region SaaS platform across AWS, GCP, and Azure. Build Kubernetes infrastructure, Terraform-based automation, Helm and ArgoCD deployment workflows, CI/CD pipelines, observability systems, and highly available data layers. Improve Kong Gateway and Mesh environments, reliability, scalability, security, disaster recovery, and cost efficiency. Participate in 24/7 on-call, incident response, SLO tracking, postmortems, and operational improvement initiatives.