Top Artificial Intelligence DevOps & Platform Engineering Jobs

Lead and grow a DevOps/infrastructure team to design, operate, and scale cloud and on-prem infrastructure (Kubernetes, IaC, CI/CD). Ensure reliability, observability, security, and customer-facing enterprise deployments while partnering across engineering, product, security, and customer success.

Lead end-to-end deployment and operation of cloud infrastructure and Symmetri environments for US customers. Provision, configure, monitor, maintain, upgrade, and troubleshoot cloud, single-tenant, on-premises, and air-gapped environments. Manage Kubernetes, networking, IAM, infrastructure as code, resource planning, incident response, runbooks, and root-cause analysis. Represent the company in customer security reviews and help establish US deployment and operations standards and teams.

The Senior DevOps Engineer leads cloud infrastructure and pipeline management, focusing on automation, CI/CD processes, system performance, and mentoring junior engineers.

Administer and sustain Microsoft 365, SharePoint, Teams, Exchange, OneDrive, collaboration platforms, and endpoint management systems across multiple government security enclaves. Manage permissions, governance, compliance, service health, incidents, patching, software deployment, imaging, and STIG-compliant operating system builds. Support Power Platform governance, RMF evidence, vulnerability remediation, change management, and vendor escalations. The role requires independent analysis, documentation, on-call participation, and up to 10% travel.

Maintain and troubleshoot an enterprise environment of approximately 800 systems and 2 petabytes of storage. Manage Linux systems, workstations, servers, networks, applications, storage, and cloud infrastructure. Configure and deploy hardware and operating systems, support users, monitor workflows, plan upgrades, coordinate outages, and participate in architecture discussions. Provide continuous operational support through a 24/7 on-call rotation.

Support secure cloud-based Linux and HPC environments for external customers using silicon design platforms. Troubleshoot compute, storage, networking, identity, scheduler, performance, and licensing issues; maintain reliability and availability; automate monitoring, provisioning, and incident response with Python, shell scripting, Perl, and agentic AI. Manage ITAR/CUI compliance, participate in root cause analysis, and create operational and customer-facing documentation.

Lead architecture and implementation for reliable, scalable AI authoring platforms. Build cloud infrastructure, CI/CD pipelines, Kubernetes and Helm deployments, infrastructure-as-code, observability, disaster recovery, A/B testing, and release processes. Improve backend reliability, performance, security, and cost efficiency across Azure and Google Cloud. Partner with product and engineering teams on technical strategy, ML infrastructure, LLM inference, and AI-assisted game development tools.

Lead cloud migration and platform engineering efforts, designing AWS architectures and migration plans from on-prem VMware to AWS. Perform migration assessments, tooling selection, wave planning, dependency analysis, security/compliance alignment, OS and database troubleshooting, and optimize post-migration performance while collaborating with stakeholders and platform teams.

Leads corporate IT and DevOps across four international sites, managing teams, SaaS applications, identity, endpoints, networking, datacenter and EDA infrastructure. Owns security, compliance, Linux systems, Kubernetes, CI/CD, developer tooling, physical IT, vendors, budgets and infrastructure modernization. Drives identity automation, OS migrations, SaaS governance, audit readiness, datacenter expansion, international integration and support for semiconductor engineering environments.

Own and improve cloud infrastructure, CI/CD pipelines, deployment tooling, monitoring, alerting, and incident response. Embed security and compliance controls for CJIS, CMMC, and SOC 2 requirements. Partner with engineering, security, product, and delivery teams to prioritize infrastructure work. Implement AI-driven DevOps automation, including remediation, anomaly detection, infrastructure-as-code generation, and compliance evidence collection.