Top Artificial Intelligence DevOps & Platform Engineering Jobs

Design, implement, and secure scalable cloud infrastructure across AWS and OCI; manage CI/CD (GitHub Actions), Kubernetes and ECS clusters (including GPU workloads); integrate observability (Datadog, Grafana, OpenTelemetry, Sentry); enforce security and compliance (SOC2, ISO 27001); handle DevOps/SRE intake, vulnerability response, and migrations from EKS to OKE.

Designs and operates scalable cloud infrastructure, distributed systems, AI training and inference platforms, backend services, media-processing pipelines, and CI/CD tooling. The role focuses on high-throughput production environments, GPU workload management, model serving, observability, incident response, and optimizing latency, reliability, and cost across user-facing AI products.

Lead enterprise Salesforce architecture and DevOps delivery across multiple organizations. Design secure, scalable architectures; implement CI/CD pipelines; automate deployments across development, QA, UAT, and production; manage Git branching, release governance, environments, metadata dependencies, testing, and rollback strategies. Provide technical leadership, architecture and code reviews, stakeholder collaboration, mentoring, and DevSecOps guidance while supporting enterprise integrations and high-quality Salesforce releases.

Own and improve Azure cloud infrastructure across compute, networking, storage, security, policy, and IAM. Build infrastructure as code using Bicep, ARM Templates, CLI, and PowerShell. Drive DevOps pipelines and CI/CD delivery while establishing cloud automation and standardization practices within a growing cloud platform team.

Operate and scale large GPU infrastructure platforms, including Linux systems, bare-metal environments, provisioning workflows, observability, and reliability automation. Responsibilities include platform deployment, incident response, break/fix operations, customer provisioning, on-call participation, infrastructure troubleshooting, and collaboration across engineering, networking, customer success, and software teams. The role also develops automation to reduce manual work and improve operational efficiency.

Design and operate a GPU management and scheduling platform for approximately 30 AI models across heterogeneous hardware. Build metrics pipelines, admission control, autoscaling, cloud orchestration, infrastructure automation, deployment pipelines, and monitoring systems. Develop production software in Python and Go, operate secure fault-tolerant cloud infrastructure, enforce healthcare security and compliance policies, troubleshoot complex issues, and mentor engineers.

Troubleshoot complex Linux, NVIDIA GPU, CUDA, Docker, virtualization, networking, hardware, and AI workload issues. Own escalated support tickets, assist clients and infrastructure suppliers, identify root causes, and collaborate with engineering teams. Build Python and Bash diagnostic tooling, create runbooks, improve monitoring and troubleshooting processes, and support supplier onboarding and machine management. The role is fully or mostly on-site in Los Angeles, with schedules covering Monday-Friday or Sunday-Thursday.

Owns the company’s IT infrastructure, including Google Workspace, identity and access, endpoint security, Meraki networking, cloud platforms, storage, backups, workstations, MDM, software licensing, and user support. Administers Rippling, Slack, Jira, GCP, Oracle Cloud, and Synology systems; maintains security controls, VPNs, MFA, and compliance evidence; manages device provisioning, patching, conferencing equipment, and disaster recovery. Provides hands-on troubleshooting and infrastructure planning in a high-growth startup environment.

Own infrastructure for deploying and operating customer-specific AI models. Responsibilities include production deployments, customer data isolation, security hardening, AWS cloud and GPU resource management, cost optimization, monitoring, logging, ETL pipelines, model versioning, ML lifecycle management, and automated testing. The role requires Python, cloud computing, CI/CD, infrastructure-as-code, security fundamentals, ML workflow familiarity, and a strong computer science background.

Own the infrastructure and platform layer for an agentic integration platform. Build and operate a durable, sandboxed integration runtime; orchestrate AI agents; develop self-healing systems, observability, and knowledge infrastructure; and support single-tenant, self-hosted deployments. Establish engineering patterns, help recruit the infrastructure team, and grow into domain leadership. The role is in-person in San Francisco and requires production Kubernetes experience and startup experience.