Senior Engineer, Compute

About the role

AHEAD builds platforms for digital business. By weaving together advances in cloud infrastructure, automation and analytics, and software delivery, we help enterprises deliver on the promise of digital transformation.

At AHEAD, we prioritize creating a culture of belonging, where all perspectives and voices are represented, valued, respected, and heard. We create spaces to empower everyone to speak up, make change, and drive the culture at AHEAD.

We are an equal opportunity employer, and do not discriminate based on an individual's race, national origin, color, gender, gender identity, gender expression, sexual orientation, religion, age, disability, marital status, or any other protected characteristic under applicable law, whether actual or perceived.

We embrace all candidates that will contribute to the diversification and enrichment of ideas and perspectives at AHEAD.

The High-Performance Computing Compute Engineer is primarily responsible for the overall health and maintenance of the infrastructure that powers high-performance computing, artificial intelligence, machine learning, and large-scale data workloads in our managed services customer environments. Our HPC Compute Engineers are a valued member of the Managed Services Infrastructure Practice responsible for Tier 3 incident management, service request management and change management infrastructure support for all Managed Services customers.

The successful candidate will own critical components of the compute environment—from Linux hosts, GPUs, HPC clusters, and physical hardware through schedulers, containers, monitoring, automation, and version maintenance. The engineer will work closely with our internal infrastructure teams, vendors, and customers to deliver reliable, secure, and efficient AI compute services.

Key Responsibilities -

Provide enterprise-level operational support to Managed Services customers for incident, problem, and change management activities

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Manage and maintain large-scale HPC and GPU compute clusters for AI training, inference, simulation, and data-intensive workloads

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Deploy and administer Linux-based compute environments, including operating systems, drivers, firmware, CUDA or equivalent accelerator software, libraries, and runtime dependencies

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Operate workload scheduling and orchestration platforms such as Slurm, Kubernetes, Ray, or comparable technologies

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Benchmark and tune end-to-end workload performance

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Troubleshoot complex failures from bare metal and firmware through the operating system, drivers, middleware, distributed frameworks, and user applications in step with other teams

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Build automation for provisioning, configuration management, patching, validation, monitoring, and lifecycle management using Python, Bash, Ansible, Terraform, or similar tools

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Develop operational dashboards, alerts, health checks, and capacity-planning processes that improve availability and resource utilization