Senior AI/ML Infrastructure Engineer

Requirements:

• 3-7 years of experience in AI/ML infrastructure, DevOps, or HPC environments.
• Strong expertise in Linux, Kubernetes, and container orchestration.
• Hands-on experience with GPUs, TPUs, or AI accelerators.
• Knowledge of storage architectures, distributed file systems (Lustre), and data lakes.
• Experience with monitoring, logging, and automation tools (Prometheus, ELK, Grafana, Python, Bash).
• Strong understanding of AI/ML frameworks (TensorFlow, PyTorch).
• Excellent problem-solving skills and ability to work in a fast-paced AI/ML environment.
• Expert level experience on C programming knowledge.

What you'll be doing:

• Architect, develop and deploy scalable AI/ML infrastructure using Kubernetes, Docker, and containerized solutions.
• Configure & Optimize GPU, TPU, and high-performance compute resources for efficient training and inference.
• Implement cloud-based AI solutions and integrate with on-prem environments.
• Design high-speed networking and storage solutions to support AI workloads. Strong understanding of RDMA, RoCE V2 protocols.
• Working and Managing experience with Nvidia SuperPOD.
• Deep understanding and hands-on experience on Parallel File Systems (Lustre).
• Optimize distributed training workflows.
• Implement automated deployment pipelines.
• Ensure cost-efficient resource utilization by tuning cloud auto-scaling, spot instances, and job scheduling.
• Develop observability and monitoring tools using Prometheus, Grafana.
• Ensure AI workloads comply with security best practices (RBAC, IAM, encryption).
• Maintain high availability, fault tolerance, and disaster recovery strategies for AI infrastructure.
• Work closely with AI/ML engineers, data scientists, and DevOps teams to streamline AI workflows.
• Stay ahead of emerging AI infrastructure trends and evaluate new technologies.

Nice to haves:

• (None specified)

Perks and Benefits:

• (None specified)