Top Artificial Intelligence DevOps & Platform Engineering Jobs

Own the CAD and engineering infrastructure supporting silicon development, including compute, storage, scheduling, cloud, security, disaster recovery, EDA tools, licensing, and RTL-to-GDS flow releases. Lead CI/CD, regression infrastructure, flow qualification, vendor strategy, and AI-assisted engineering adoption. Ensure scalable, reproducible, secure platforms support concurrent SoC programs and thousands of weekly jobs while partnering with RTL, verification, DFT, physical design, packaging, and software teams.

Own enterprise customer technical cases from triage through resolution, troubleshooting Kubernetes, ECS, Docker, cloud infrastructure, deployments, connectivity, and CI/CD issues. Perform root-cause analysis, lead customer calls, build diagnostic scripts and tooling, contribute small product fixes, and document reusable runbooks. The role also includes occasional customer build engagements and collaboration with Product and Engineering.

Leads end-to-end AWS cloud engagements, setting technical strategy, designing foundational multi-account environments, and directing large-scale migrations. Builds and reviews infrastructure as code, networking, security baselines, and hybrid connectivity using AWS services, Terraform, and CDK. Coordinates distributed engineering teams, mentors consultants, manages customer technical relationships, drives architecture decisions, and oversees rehost and replatform migrations through production readiness. Requires deep hands-on AWS expertise, customer-facing leadership, and an active AWS Certified Solutions Architect Professional certification.

Build and operate Sauna’s stateful agent runtime, including session state, tool-call retries, timeouts, concurrency, actor-versus-job execution, and recovery across deployments. Develop production backend systems that resume interrupted work, trace runtime failures, and support long-running processes through code releases. The role requires hands-on TypeScript development using Cloudflare Workers, Durable Objects, Postgres, and Docker.

Own and scale Cognitiv’s AWS infrastructure while evaluating architecture, networking, security, scalability, and service management. Lead improvements in deployments, monitoring, disaster recovery, and infrastructure-as-code practices. Support co-located Equinix datacenter deployments and hybrid cloud operations alongside a datacenter-focused SRE. Collaborate with engineering and product teams, provide multi-datacenter coverage, and help establish long-term service management best practices.

Own and improve infrastructure across cloud, customer-managed cloud, on-premises, and air-gapped environments. Package the platform as a reproducible Kubernetes bundle, operate application and GPU workloads, manage infrastructure as code and CI/CD releases, and build monitoring, logging, and alerting for restricted environments. Responsibilities also include supporting search and data infrastructure, networking, secrets, certificates, and security hardening.

Design and scale Macroscope’s distributed backend systems, cloud infrastructure, APIs, CI/CD pipelines, observability, and production operations. Build infrastructure for large-scale LLM training, fine-tuning, and inference workloads in partnership with ML researchers. Lead architectural decisions and infrastructure projects, improving system reliability, performance, and scalability while working with Go, GCP, Terraform, PostgreSQL, Temporal, and related technologies.

Lead the cloud infrastructure engineering team, owning platform reliability, roadmap, costs, capacity, vendor relationships, observability, incident response, and compliance readiness. Manage hiring, performance, and career development while partnering with product, device, data, and security teams. Establish standards across AWS, Kubernetes, Terraform, GitOps, CI/CD, and secure device-to-cloud communication as the company scales.

Own the platform infrastructure supporting inference services, data pipelines, agents, and UI applications. Design cloud infrastructure, CI/CD, infrastructure-as-code, deployment automation, autoscaling, security controls, observability, developer tooling, and incident response. The role supports AWS or other major cloud environments, Kubernetes workloads, GPU and CPU scaling, secure delivery pipelines, and reliable production operations.

Owns infrastructure, deployment, reliability, and observability for multi-cloud AI systems and autonomous agents. Responsibilities include Terraform-based infrastructure across AWS, Azure, and GCP; managed Kubernetes; incident response; SLOs; on-call operations; compliance automation for HIPAA and SOC 2; customer environment provisioning; and developer deployment platforms. The role also operates observability tooling and supports production AI, inference, and research-to-production workflows.