The AI Data Center Infrastructure Engineer is responsible for the operational management, monitoring, maintenance, reliability, and optimization of AI-ready data center infrastructure supporting high-density GPU, HPC, and AI workloads.
Responsibilities
Monitor and operate critical data center infrastructure including electrical, mechanical, and IT support systems
Ensure availability and reliability of power, cooling, network, and server room environments
Manage day-to-day operational activities across AI infrastructure facilities
Support continuous operations in a 24x7 mission-critical environment
Support AI clusters, GPU servers, HPC systems, and storage platforms
The AI Data Center Infrastructure Engineer is responsible for the operational management, monitoring, maintenance, reliability, and optimization of AI-ready data center infrastructure supporting high-density GPU, HPC, and AI workloads.
Responsibilities
Monitor and operate critical data center infrastructure including electrical, mechanical, and IT support systems
Ensure availability and reliability of power, cooling, network, and server room environments
Manage day-to-day operational activities across AI infrastructure facilities
Support continuous operations in a 24x7 mission-critical environment
Support AI clusters, GPU servers, HPC systems, and storage platforms
Work with product team on the shared full stack ownership of a collection of services and/or technology areas. Responsible for the mitigating critical customer incidents, or deployments or testing required to improve security, performance, availability, and scalability of service.
Compensation
IN: Hiring Range in INR from: 1,393,900 to 2,439,400 per annum.
Responsibilities
Independently troubleshoot and resolve production issues involving Linux hosts, GPU systems, and host-network interactions.
Support GPU host operations across provisioning, validation, incident response, and operational readiness activities.
Own incident response and resolution for assigned services while escalating appropriately when problems cross team or domain boundaries.
Identify recurring failure patterns and contribute improvements to runbooks, tooling, monitoring, and operational procedures.
Work with partner teams to resolve infrastructure issues that span hardware, operating systems, networking, and service dependencies.
Help design, deploy, automate, and operate CPU and GPU infrastructure supporting high-performance computing, distributed AI training, and inference workloads across on-premises and cloud environments.
Responsibilities
Deploy, configure, and maintain Linux-based CPU and GPU clusters following established architecture and security standards
Administer Slurm and Kubernetes environments, including scheduling, resource allocation, access controls, and workload troubleshooting
Support distributed AI training and inference platforms by managing software environments, containers, and workload deployments
Automate infrastructure provisioning, configuration, and lifecycle management using infrastructure as code, configuration management, and scripting
Validate hardware, BIOS, firmware, GPU drivers, and runtime compatibility and support upgrades through testing, rollback planning, and user communication
Requirements
Bachelor’s or master’s degree in computer science, Computer Engineering, or a related field or an equivalent combination of education and relevant professional experience
The Senior AI Architect will play a key role in shaping and advancing the organization's enterprise AI architecture and technology strategy, bridging business and clinical demands with enterprise technology capabilities.
Responsibilities
AI strategy and roadmap
Translate business and clinical priorities into AI direction
Evaluate technologies, adoption models, build-versus-buy and sovereign AI options within the hybrid strategy
Define architecture across on-premises, private and public clouds, including GPU infrastructure, model hosting, containers, cloud AI and integration
Set patterns for workload placement, deployment, orchestration and connectivity, addressing security, resilience, cost and data residency
Build the control plane and data plane for physical AI, designing and operating large-scale distributed systems across cloud, on-prem, robotics labs, and edge devices.
Responsibilities
Build a unified execution platform for simulation, policy evaluation, model training, data processing, and deployment
Design workload scheduling and orchestration across CPUs, GPUs, Kubernetes clusters, bare metal, on-prem environments, and edge devices
Build reliable systems for checkpointing, retrying, resuming, scaling, and observing long-running robotics workloads
Create the data infrastructure for video, audio, sensor streams, telemetry, trajectories, policy traces, incidents, and human interventions
Design storage, indexing, lineage, replay, retention, and movement of large multimodal datasets
Lead and own day‑to‑day operations for US neocloud customers, managing uptime, incident response, capacity, and customer outcomes for GPU‑dense data centres and Kubernetes‑based platforms.
Responsibilities
Own operational delivery for US neocloud customers: availability, SLAs, incident and problem management, and change control.
Lead and develop the US operations team, including engineers, NOC/SRE staff and site‑level coordinators. Set hiring plans, on‑call structure and performance standards.
Run GPU cluster operations: health monitoring, firmware and driver lifecycle, fabric and storage performance, node replacement and capacity planning.
Act as leadership escalation point for major incidents and lead the communication with customer executives.
Work with customer data centre and engineering teams to define runbooks, handover processes and operating models.
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