Top Artificial Intelligence DevOps & Platform Engineering Jobs

Own infrastructure reliability, scalability, and developer experience: author IaC (OpenTofu/Terraform), design/manage Kubernetes and Helm charts, architect CI/CD, implement observability (Grafana/Prometheus/OpenTelemetry), manage secrets and production networking, optimize container builds, and maintain developer tooling across environments.

Design and build scalable infrastructure while mentoring teammates and collaborating on AI and product solutions. Drive best engineering practices.

Responsible for designing and building scalable backend services, maintaining cloud infrastructure on GCP, and integrating with utility data sources, among other tasks.

The role involves designing and managing distributed systems infrastructure for AI workloads, optimizing data pipelines, and collaborating on machine learning projects.

As a Senior Deployment Engineer, you'll manage the deployment lifecycle of Atolio's enterprise search platform, ensuring smooth, secure, and effective client deployments while collaborating across teams and building automation tools.

Design and build scalable log ingestion pipelines, ensure system reliability, and optimize performance in a high-throughput environment.

Seeking Data Platform Engineers to design, build, and manage scalable data pipelines and analytic query engines while ensuring data integrity and performance optimization.

Seeking a skilled DevOps Engineer to manage AWS cloud infrastructure for ASP.NET applications, automate deployments, and enhance cloud efficiency with GitLab CI/CD pipelines.

Design, build, and operate backend systems and infrastructure. Work on system design, databases (SQL/NoSQL), cloud platforms, containerization, IaC, CI/CD, monitoring/logging, networking, security, and Linux fundamentals in a fast-paced startup environment.

Lead design, build, and operate the company data platform: APIs, web services, data pipelines, data warehousing, infrastructure tooling, monitoring, deployment systems, and production support. Manage and mentor a platform team, set goals, track metrics, and collaborate with engineering and research teams to deploy algorithms and troubleshoot production issues.