Top Artificial Intelligence DevOps & Platform Engineering Jobs

Own the platform infrastructure supporting inference services, data pipelines, agents, and UI applications. Design cloud infrastructure, CI/CD, infrastructure-as-code, deployment automation, autoscaling, security controls, observability, developer tooling, and incident response. The role supports AWS or other major cloud environments, Kubernetes workloads, GPU and CPU scaling, secure delivery pipelines, and reliable production operations.

Owns infrastructure, deployment, reliability, and observability for multi-cloud AI systems and autonomous agents. Responsibilities include Terraform-based infrastructure across AWS, Azure, and GCP; managed Kubernetes; incident response; SLOs; on-call operations; compliance automation for HIPAA and SOC 2; customer environment provisioning; and developer deployment platforms. The role also operates observability tooling and supports production AI, inference, and research-to-production workflows.

The MSP Sr. Developer leads a team to maintain and improve AWS environments, manage incidents, execute change requests, and mentor junior developers while ensuring operational stability and client satisfaction.

Build and maintain backend platform infrastructure enabling governments and enterprises to acquire technology rapidly. The role focuses on DevOps and backend infrastructure using tools such as Python, Linux, Kubernetes, Terraform, cloud platforms, and LLM tooling. Candidates should have 2–8 years of backend infrastructure experience and a passion for national security problems. Government project experience and Secret or TS/SCI clearance are bonuses. The role is on-site in San Francisco or Washington, DC.

Own Mariana Minerals’ infrastructure platform across AWS, EKS, on-premises, and edge clusters. Build self-service infrastructure, GitOps delivery, observability, identity and secrets management, cost controls, and reliable ML/data workloads. Lead cluster operations, incident response, upgrades, and automation that enables engineers to provision and operate services independently.

Owns the reliability, performance, and operation of a high-density GPU compute cluster. Responsibilities include Linux administration, cluster monitoring, GPU deployment and driver management, kernel and hardware debugging, network security, identity and distributed storage administration, and maintenance of CUDA, ROCm, PyTorch, JAX, vLLM, and related ML infrastructure. The role also handles incident response, vendor coordination, performance tuning, and automation for large-scale AI infrastructure.

Own and scale Rebar’s cloud infrastructure and production operations across AWS and Kubernetes. Responsibilities include improving infrastructure and application performance, managing SOC 2 controls, implementing observability, completing environment partitioning, optimizing cloud spending, maintaining CI/CD, and mentoring engineers. The role requires operating distributed systems, containerized workloads, and production cloud environments while supporting migrations and continued company growth.

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.