Top Artificial Intelligence DevOps & Platform Engineering Jobs

Evaluate AI-generated web traffic and cloud infrastructure forecasts, assess accuracy and quality using a rubric. Require analytical skills and experience in web ops.

The Founding Cloud Infrastructure Engineer will develop and maintain the cloud platform's infrastructure using GoLang and Kubernetes, solve client issues, and manage production systems.

The Senior ML Infrastructure Engineer will own and optimize GPU infrastructure for training tabular foundation models, ensuring high efficiency and collaboration with research teams.

The Solutions Architect will provide architectural leadership, guide engineering decisions, and shape long-term technology strategy, focusing on cloud infrastructure, DevOps practices, and system evolution.

The Infrastructure Engineer will design and develop scalable infrastructure and developer tools for KB AI. Responsibilities include setting technical direction and participating in company culture building.

The role involves benchmarking infrastructure performance, designing tests, debugging customer trials, and maintaining benchmarking infrastructure while ensuring clear documentation and communication.

The Infrastructure Engineer will ensure the reliability, security, and performance of Thatch's healthcare platform infrastructure. Responsibilities include designing infrastructure, improving CI/CD tools, and managing security and reliability across the stack.

The Deployment Engineer will support engineering during development and deployment at customer sites, develop testing plans, execute tests, analyze data, and communicate feedback.

Lead and hands-on manage Seekr's AI infrastructure: design, build, and scale Kubernetes-based systems across cloud, on-prem, and edge; mentor engineers; automate operations; handle incidents and security; collaborate with software teams to ensure reliability, performance, and observability.

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.