Top Artificial Intelligence DevOps & Platform Engineering Jobs

Design, build, and operate a multi-cluster, multi-tenant Kubernetes fleet for GPU inference. Extend Kubernetes with controllers/CRDs, implement GPU scheduling and autoscaling, own the network data plane, design cross-cluster connectivity and service mesh, drive reliability/SLOs, deliver IaC with Terraform/Helm/GitOps, and collaborate with platform, SRE, and security teams.

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

Build and own the platform: implement CI/CD, shared dev/staging environments, deployment hosting, API gateway and auth middleware, and audit/event logging pipelines. Codify infrastructure as code, design deployment workflows, ensure observability, and define operational patterns for the engineering team.

Design, build, and secure scalable infrastructure and data ingestion pipelines for an enterprise AI platform handling sensitive financial data. Implement private-cloud, tenant-isolated deployments, access controls, monitoring/alerting, CI/CD, and infrastructure-as-code. Partner with AI research/product teams to optimize for LLM training and inference workloads.

Own and operate the AI-native platform runtime including Kubernetes (GKE), Terraform, GCP, CI/CD, observability, orchestration (Temporal, Kong). Improve reliability, performance, cost, multi-tenant provisioning, developer experience, and lead on-call and incident response.

Maintain and improve CI/CD pipelines, manage build artifact storage, operate cloud and GPU clusters, enhance MLOps training/deployment and observability, and harden security and multi-tenancy for robot infrastructure.

Design and implement AWS infrastructure and hybrid cloud solutions (VPC, S3, EBS, CloudWatch, CloudTrail, IAM, Redshift, HSM). Work with virtualization and VMware (vRealize/vRO/vRA), networking (DNS, IP management, VPC peering), ITIL practices, and automation via shell scripting and Python. Support cloud deployments and integration with Azure/Google Cloud. AWS architect certification is a plus.

Lead technical vision as a principal engineer, either managing teams or driving cross-team initiatives. Design and architect cloud infrastructure, networking, and security; define authentication/authorization patterns; architect and operate Kubernetes deployments; and implement infrastructure-as-code using tools like Terraform, CloudFormation, Ansible, or Puppet.

Design, build, and operate low-latency, highly available cloud infrastructure for real-time voice AI. Own Kubernetes clusters end-to-end, implement IaC, CI/CD and GitOps (ArgoCD), maintain observability and security, optimize performance and costs, lead incident response, and partner with engineering, product, and support teams.

Build and operate scalable, secure cloud infrastructure for real-time AI services. Improve reliability with SLOs, observability, capacity planning, and failure testing. Implement IaC, CI/CD, deployment safety, secrets and vulnerability management. Partner with product and AI teams, diagnose distributed failures, reduce cost, and automate platform patterns to enable faster, safer releases.