Top Artificial Intelligence DevOps & Platform Engineering Jobs

Lead design and implementation of Astronomer’s multi-cloud platform infrastructure. Own platform strategy, build production-grade distributed systems (Kubernetes/Go), make architecture decisions, document designs and postmortems, and drive cross-team technical choices to scale a SaaS/PaaS offering for enterprise customers.

Design, implement, and maintain Terraform-based Infrastructure as Code and Azure environments. Build and optimize CI/CD pipelines, automate deployments, enable containerized microservices on Kubernetes, ensure security, monitoring, and networking best practices, troubleshoot infrastructure issues, and document standards while collaborating with distributed development teams.

As a Platform Engineer, you will design, build, and optimize an orchestration platform; develop backend services, and ensure performance and security of complex workflows.

Seeking a DevOps Consultant for project management and tracking using JIRA and Rally, code management with GitHub, and various CI/CD tools for deployment and configuration management in Azure.

Design, build, and maintain infrastructure for an adtech platform. Operate production systems at scale, optimize performance, own platform layers, run experiments, and ensure reliability across a broad set of systems.

Seeking a Lead DevOps Engineer for deploying health IT solutions on AWS, with expertise in CI/CD, Terraform, and Windows environments. Must have experience leading teams and managing DevOps practices.

Develop and maintain scalable web applications using React and FastAPI, implement DevOps practices, and collaborate on AI/ML projects.

Own the reliability and performance of backend systems at Gamma, building automation and tooling while leading incident response and improving system stability.

Own reliability for a live fleet of Linux-based edge sensors and cloud infrastructure. Triage and recover field hardware, perform SSH-based diagnostics, build fleet management and OTA systems, implement observability and alerting, automate operational tasks, develop runbooks, and participate in on-call rotations to prevent and resolve incidents.

Lead architecture and implementation of scalable ML data pipelines and tooling to turn autonomy data into training datasets. Build data selection, active learning, inference-at-scale, vector DB management, labeling integration, QC automation, and introspection tools while partnering with ML researchers and infra teams.