Top Artificial Intelligence DevOps & Platform Engineering Jobs

Build and maintain reliable, scalable platforms and services. Responsibilities include monitoring, incident response, post-mortems, disaster recovery testing, production automation, AI service deployment, infrastructure documentation, and reliability testing. The role requires advanced Terraform, AWS operations, GitHub Actions CI/CD, containerized ECS Fargate environments, networking and security expertise, Linux and scripting skills, observability, and developer enablement across engineering teams.

Deploys, maintains, and scales GitLab-centered engineering platforms and tooling. Designs complex CI/CD pipelines for container, Helm, RPM, and cross-platform builds; manages artifact registries; integrates SAST, code quality, vulnerability scanning, automated testing, and security controls. Establishes development standards, branch strategies, commit conventions, code review practices, pre-commit hooks, project scaffolding, signing, and secret management. May also build machine learning specialization pipelines.

Own infrastructure for deploying and operating AI agent systems across customer environments. Responsibilities include workflow reliability, CI/CD, secrets and access controls, database performance, observability, integration resilience, incident response, and on-call operations. The role works closely with engineers to build practical shared infrastructure, investigate production failures, and improve systems across deployments. This is an in-person San Francisco position with occasional extra hours and weekend commitments.

Leads the SRE function and team, establishing reliability practices, observability standards, automation, incident management, and 24x7 operations. Develops scalable monitoring and AIOps architectures, modernizes hybrid cloud and enterprise network connectivity, and partners with infrastructure, engineering, cybersecurity, and service providers. Provides architectural guidance, drives migration and continuous improvement, and reports reliability and operational maturity to leadership.

Build and optimize infrastructure, developer tooling, build systems, CI/CD, APIs, services, and deployment platforms. Investigate performance bottlenecks across software, operating systems, storage, networks, and hardware; improve observability, reliability, provisioning, and recovery. Develop internal platforms used by multiple engineering teams, maintain homelab infrastructure, and measure the impact of performance and workflow improvements.

Build and operate Zoox’s continuous deployment platform, including its paved path, reliability, safety controls, and adoption across application teams. Partner with SRE and engineering teams to migrate services, turn recurring deployment friction into platform capabilities, establish safe production release standards, and mentor engineers. The role requires expertise in platform or infrastructure engineering, Kubernetes, container deployments, continuous delivery systems, programming, and infrastructure as code.

Build Probook’s infrastructure foundation across agent development, developer tooling, real-time AI products, distributed systems, reliability, observability, and cloud optimization. The role owns infrastructure end to end, including agent sandboxes, evaluation systems, CI/CD, deployment workflows, workflow orchestration, production reliability, and performance. This is a greenfield, in-office role requiring broad technical ownership and close collaboration in a fast-growing startup.

Architects and owns highly available infrastructure and Kubernetes-based platforms supporting autonomous systems. Builds Golang backend services, platform tooling, observability systems, dashboards, alerts, and log aggregation. Partners with product teams to launch services, performs performance analysis, manages cloud upgrades, and participates in incident response and postmortems. Collaborates on cloud security risk assessments, intrusion detection, threat-feed systems, risk mitigation, and SaaS payment processes. Provides architectural leadership and mentorship across engineering teams.

Leads the architecture, engineering, and delivery of enterprise Google Cloud solutions spanning cloud-native platforms, data platforms, AI, APIs, integrations, and application modernization. Designs secure GCP architectures, landing zones, governance models, and reusable automation. Advises executive stakeholders, leads architecture workshops and transformation programs, supports customer pursuits, and mentors architects and engineers while developing cloud, AI, data, and integration capabilities.

Own and evolve the cloud foundation supporting AWS accounts, IAM, networking, DNS, governance, and Terraform execution. Build self-service infrastructure controls, automate compliance and access management, establish scalable platform patterns, and improve cost visibility. Partner with Security, GRC, IT, and engineering teams while operating reliable, observable systems and participating in on-call support.