Top Tech Jobs & Startup Jobs

Lead design and operation of CI/CD and MLOps pipelines, cloud-native infrastructure, and observability. Own incident response, security/compliance, IaC, Kubernetes/Docker cluster management, GPU provisioning for AI workloads, high-availability and disaster recovery, and cross-team platform engineering to improve developer productivity and reliability.

Monitor GPU cluster, network, storage and environmental systems; respond to alerts and follow runbooks; perform hardware triage and standard remediation; collect diagnostics for escalation; manage incident tickets; perform physical data-center tasks (rack, cabling, hardware swaps); execute shift handoffs and maintain runbooks; assist with deployments and firmware under SME guidance.

Design and build global control-plane services for a GPU/AI cloud: tenant management, quota, metering/billing, RBAC, ticketing, customer portal/API, automation, region-aware scheduling, and operational tooling for detection and recovery.

Provide first-line cloud and NOC support for a multi-region GPU cloud: handle tickets, monitor alerts, triage and resolve issues across GPU instances, VMs/bare metal, networking, storage and billing, maintain runbooks, escalate to L3/SRE when needed, and participate in 24/7 follow-the-sun shift rotations.

Own and resolve complex escalations and platform incidents end-to-end. Troubleshoot GPU compute, networking/SDN, storage, drivers/CUDA, control plane, and billing. Drive root-cause analysis with SRE/Compute/R&D, lead incident response and post-incident reviews, improve runbooks and monitoring, mentor L1/L2, and participate in on-call escalation rotation.

Design, implement, and maintain CI/CD and MLOps pipelines, provision and scale cloud-native AI infrastructure (Kubernetes, GPU clusters), enforce IaC practices, implement observability and security frameworks, lead incident response and cross-functional platform engineering to ensure high availability and compliance.

The AI Infrastructure Engineer is responsible for architecting and managing AI compute environments using GPU clusters and optimizing performance across networking and storage systems.

The role involves ensuring reliable system integration and operation across various technical systems in a large-scale AI data center while collaborating with multiple engineering disciplines.

Lead end-to-end architecture of AI data center networks, DCI, and global backbone for large GPU clusters. Design underlay and overlay networks, IP/VLAN/VXLAN planning, congestion control (PFC/ECN/INT), and optical transport. Produce HLD/LLD, topology diagrams, standards, and SOPs; engage vendors and drive risk mitigation and network improvements.

Design and produce visual assets and motion graphics for digital, print, and video campaigns. Ensure brand consistency, collaborate with marketing/product teams, manage multiple projects, prepare final files for production, and maintain an organized asset library.