Top Artificial Intelligence DevOps & Platform Engineering Jobs

Build and operate infrastructure connecting ruggedized wearable devices, edge networks, cloud systems, video pipelines, and AI compute environments. Develop petabyte-scale ingestion and storage, edge and cloud VLM inference, research compute orchestration, automated reporting, privacy controls, and fleet observability. Solve challenging networking, data transport, storage, distributed systems, hardware-software integration, and multi-environment ML infrastructure problems while scaling construction video processing to millions of hours.

Lead software release readiness, integration, deployment, and production stabilization for warehouse automation systems. Build and support CI/CD pipelines, Kubernetes deployments, infrastructure automation, tools, and operational processes. Troubleshoot complex application, infrastructure, database, and distributed-system issues; mentor engineers; and coordinate with Engineering, DevOps, QA, Operations, site teams, and customers. Travel 10–20% for deployment activities and release events.

Build, operate, and optimize production inference systems for large models. Own throughput, latency, cost-per-token, reliability, profiling, optimizations (quantization, custom kernels), observability, autoscaling, load testing, incident response, and postmortems.

Design, build, and maintain cloud infrastructure, IaC, and CI/CD for serverless and containerized services. Improve developer experience with internal tooling and self-serve abstractions. Define observability standards, investigate performance bottlenecks, support incident response, and automate security and compliance workflows. Participate in on-call rotation and postmortems to improve reliability and operational excellence.

Design and implement robust infrastructure to support rapidly growing usage, deploy Greptile for enterprise customers, and operationalize on-premise deployments. The role involves cloud engineering, networking, containerization, virtualization, Kubernetes, Terraform, Linux systems, and sandboxed execution environments. Engineers will collaborate closely with customers and help maintain reliable infrastructure for an AI-powered code review platform.

Design and evolve core control, data, and observability systems; implement resilient region-spanning architectures; build libraries, protocols, and tooling; diagnose and harden critical paths using metrics, tracing, and testing; and shape platform capabilities across identity, scheduling, observability, and distributed state management.

Design, implement, and operate low-latency, highly available distributed control, data, and observability systems. Build region-spanning architectures, libraries, protocols, and tooling; diagnose and harden critical paths using metrics, tracing, testing, and real traffic; and shape platform capabilities around identity, scheduling, observability, and distributed state management.

Lead the development of a platform for fast, high-quality deployments in universities, focusing on security, data engineering, and automation.

Design, deploy, and maintain physical and cloud network infrastructure for a large-scale AI data platform. Implement IaC, virtualization, container orchestration, monitoring, and security to ensure scalable, reliable operations and integrate DevOps automation with engineering teams.

Lead and grow the Platform engineering team building APIs, workflows, and interfaces that make simulations usable. Set technical direction, make architecture decisions, write code, turn research into reliable product features, hire and mentor engineers, and deliver high-quality production systems in an in-person NYC environment.