• Push the limits of performance with GPU & accelerator optimization (cloud + hardware).
• GenAI is mandate
• Proficient in Python, Databricks and ETL Pipeline
• Pinecone and MLops
• GPU

Must Have

• Expertise in Microsoft Azure Architecture
• Strong experience designing and operating enterprise-scale cloud infrastructure.
• Deep understanding of AI infrastructure components and their integration.
• Proficiency with Infrastructure-as-Code (Terraform, Ansible).
• Strong experience with Kubernetes and containerized platforms.
• GPU & Accelerator Optimization (Cloud + Hardware)
• Experience of GPU utilization, memory bandwidth, interconnects
• Choosing the right instance types, accelerators, and scaling models

Good to Have

• Experience with GPU/accelerator platforms (NVIDIA, AMD, custom accelerators).
• Knowledge of distributed AI training frameworks and high-performance computing (HPC) concepts.
• Exposure to MLOps platforms, model lifecycle management, and AI pipelines.
• Familiarity with multi-cloud or hybrid cloud strategies (AWS, GCP, Oracle Cloud).
• Experience with FinOps practices for AI workloads.
• Understanding of data locality, high-throughput networking (RDMA, InfiniBand), and parallel file systems.
• Ability to translate AI workload requirements into infrastructure reference architectures.