Top Artificial Intelligence DevOps & Platform Engineering Jobs

Build and maintain production data pipelines and models to support analytics, ML, and research. Ensure data quality and availability, operate and monitor the data platform, investigate failures, optimize performance and costs, and collaborate with Product, Engineering, and Research teams.

Design and build the cloud platform integrated with the database runtime to enable fast workload placement, elastic scaling, multi-tenant isolation, automated provisioning, upgrades, recovery, observability, and cost-efficient execution across Kubernetes and multi-cloud environments.

Own technical implementation and onboarding for customers from evaluation through post-sale: configure, integrate, and deploy the product, run POCs, build repeatable playbooks, diagnose complex integration issues, and collaborate with Sales, Product, and Engineering to accelerate time-to-value.

Lead and evolve the core platform architecture powering n8n: design scalable distributed execution, persistence and data foundations; define platform interfaces; drive RFCs and migrations; enable and mentor engineering teams; ensure reliability, tenant isolation, and operability across cloud and self-hosted deployments.

Build and own the end-to-end platform that runs realtime AI-human sessions: provisioning, streaming performance, cost per session, security/isolation, reliability, and observability. Provide self-service tooling for engineers and deployment teams, implement SLOs and incident response, and scale browser-based sessions while enforcing enterprise data-handling standards.

Support daily build and deploy activities for a Bank of America application: automate CI/CD pipelines, manage pre-prod and production deployments, compile and branch code, troubleshoot failures, improve CI efficiency, coordinate with product and engineering teams, and document processes.

Design, build, and maintain core platform services, developer tools, automation, and infrastructure-as-code. Improve CI/CD, release automation, and cloud provisioning. Own features end-to-end and collaborate with engineering and product teams to enhance scalability, reliability, and developer productivity.

Own and evolve Clinically AI's cloud infrastructure and deployment systems on GCP. Build and maintain Terraform IaC, Helm charts, GKE/containerized deployments, and GitHub Actions CI/CD. Implement observability, security best practices, cost and performance optimizations, and participate in on-call incident response while partnering with backend, AI, and product teams.

Design, build, and maintain scalable AWS infrastructure using IaC. Improve CI/CD pipelines, implement monitoring and observability, automate routine infrastructure tasks, manage cross-account AWS environments, and advise engineering teams on best practices to ensure reliability, security, and performance.

Design, operate, and evolve ElastixAI's Kubernetes and multi-cloud inference infrastructure. Run accelerated ML workloads at scale, build deployment and automation tooling, harden AWS/GCP/on-prem systems, partner with ML/runtime teams to productionize models, optimize costs and reliability, and participate in on-call rotation.