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Tier-1 brand and Bangalore metro increase applicant density despite senior, niche specialization.
High because skills are specialized to AI/HPC, GPUs, RDMA, and distributed training infrastructures.
High due to an explicit 8+ years requirement and specialized GPU/HPC and AI infrastructure skills.
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Own installation, configuration, and performance optimization of GPU-based AI infrastructure including compute, storage, and networking components.
Analyze system telemetry, profiling, and performance metrics to identify and implement workload optimization for AI training and inference on HPE platforms.
Collaborate with customers, partners, and internal teams to deliver scalable, high-performance AI solutions; author technical documentation and lead performance engineering best practices.
Typically 8+ years of experience in AI performance engineering or related HPC fields.
Strong Linux system administration skills on enterprise distributions.
Experience with AI/ML frameworks including PyTorch, JAX, Hugging Face Transformers, and benchmarking AI workloads.
Proficiency in programming/scripting languages such as Python, Bash, Go, or C++.
Senior or principal-level engineer with deep expertise in multi-GPU and distributed AI environments using modern interconnects like InfiniBand and RDMA.
Experienced in performance profiling, distributed training/inference, containerized AI/ML environments (Docker, Kubernetes), and large language model workflows.
Proven ability to independently evaluate emerging AI technologies, develop reference architectures, and communicate complex performance analyses clearly to diverse stakeholders.