Infrastructure AI Engineer

About the Role

We are looking for an Infrastructure AI Engineer to join our SRE team and build, deploy, and optimize high-performance cloud and bare-metal infrastructure for AI/ML and LLM workloads.

Key Responsibilities

• Design and manage scalable infrastructure for AI model training and real-time inference.
• Manage GPU/TPU clusters, Kubernetes, Ray, Slurm, and distributed storage.
• Build AIOps capabilities using Prometheus, Datadog, OpenTelemetry and automated anomaly detection.
• Develop self-healing infrastructure and automated remediation workflows.
• Define and monitor SLOs, SLAs, and Error Budgets.
• Build CI/CD and MLOps infrastructure for continuous model deployment and validation.
• Optimize GPU/CPU utilization, infrastructure performance, and cloud costs.
• Perform capacity planning, load testing, and stress testing for large-scale inference workloads.

Required Skills

• 6+ years in SRE, Systems Engineering, Infrastructure Engineering, or Cloud Platform Engineering, preferably with AI/ML workloads.
• Strong hands-on experience with Kubernetes, containers, and GPU provisioning.
• Advanced experience with Terraform, Pulumi, Ansible, or CloudFormation.
• Programming experience in Python, Go, or C++.
• Strong knowledge of Prometheus, Grafana, OpenTelemetry, and modern observability platforms.

Good to Have

• Experience with vLLM, TensorRT-LLM, or Triton Inference Server.
• Knowledge of DeepSpeed, Megatron-LM, or Ray Cluster.
• Understanding of NVLink, InfiniBand, RoCE, GPU interconnects, and memory optimization.
• Exposure to Pinecone, Milvus, Qdrant, or other vector databases.