Senior AI Infrastructure & Platform Engineer
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Key skills for this role
About the Role
Deepsource Technologies seeks a Senior AI Infrastructure & Platform Engineer to deploy, maintain, and optimize GPU-based compute clusters and infrastructure. The role involves managing Nvidia AI tools, Slurm, Kubernetes, and collaborating with data science teams.
Key Skills for This Role
Responsibilities
- Deploy, maintain, and optimize GPU based compute clusters and infrastructure
- Manage and operate GPU orchestration tools such as Nvidia Base Command Manager and Nvidia AI Enterprise Suite
- Configure, deploy, and maintain compute workloads using Slurm and Kubernetes
- Install, configure, and maintain underlying OS (e.g., Ubuntu) and supporting system software
- Monitor and troubleshoot infrastructure performance, availability, and reliability
- Work with data scientists and ML engineers to define infrastructure requirements and deployment workflows
- Develop automation scripts, CI/CD pipelines, and best practices for infrastructure provisioning
- Document architecture, configurations, and operational procedures
Requirements
- Proven experience managing GPU based AI/ML infrastructure and compute clusters
- Hands on experience with Nvidia Base Command Manager, Nvidia AI Enterprise Suite, Nvidia GPU/Network Operators, NIMs, Blueprints
- Strong experience with Slurm and/or Kubernetes orchestration
- Solid Linux system administration skills (Ubuntu preferred)
- Strong scripting/automation ability (Bash, Python)
- Excellent troubleshooting and performance tuning skills
- Experience collaborating with ML/data science teams
Full Job Posting
Key Responsibilities
- Deploy, maintain, and optimize GPU based compute clusters and infrastructure.
- Manage and operate GPU orchestration tools and platforms such as Nvidia Base Command Manager, Nvidia AI Enterprise Suite, Nvidia GPU and Network Operators, Nvidia NIMs and Blueprints.
- Configure, deploy, and maintain compute workloads using scheduling and orchestration tools including Slurm and Vanilla Kubernetes.
- Install, configure, and maintain the underlying OS (e.g. Canonical Ubuntu) and supporting system software.
- Monitor and troubleshoot infrastructure performance, availability, and reliability; ensure high uptime for AI/ML workloads.
- Work with data scientists, ML engineers, and dev teams to define infrastructure requirements, resource allocation, and deployment workflows.
- Develop automation scripts, CI/CD pipelines, and best practices for infrastructure provisioning and management.
- Document architecture, configurations, and operational procedures; enforce security, compliance, and backup policies.
Required Skills & Experience
- Proven experience managing GPU based AI/ML infrastructure and compute clusters.
- Hands on experience with Nvidia Base Command Manager, Nvidia AI Enterprise Suite, Nvidia GPU/Network Operators, NIMs, Blueprints.
- Strong experience with Slurm and/or Kubernetes orchestration.
- Solid Linux system administration skills preferably on Ubuntu or similar distributions.
- Strong scripting/automation ability (e.g. Bash, Python, or relevant tooling) for provisioning, deployment, and maintenance.
- Excellent troubleshooting and performance tuning skills.
- Experience collaborating with ML/data science teams and integrating infrastructure with their workflows.
- Strong understanding of networking, security, resource allocation, and cluster management best practices.
Preferred Qualifications
- Previous experience working in a high performance computing (HPC) or AI focused infrastructure team.
- Knowledge of containerization, container orchestration, and GPUs in cloud or on prem environments.
- Experience with CI/CD, infrastructure as code (e.g. Terraform, Ansible), monitoring tools, and logging setups.
- Familiarity with workload scheduling, job queuing, resource quotas, and GPU shared environments.
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