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Metro location and recognizable enterprise brand increase candidate competition balanced by niche MLOps specialization.
Role requires combined deep ML and infrastructure expertise, limiting cross-industry transferability.
Multiple mandatory niche technologies and senior role expectations raise strictness.
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Lead design and automation of end-to-end MLOps pipelines integrating Pure Storage platforms with open-source tools like Kubeflow and MLflow for enterprise AI/ML solutions.
Build and deploy high-performance AI/ML reference architectures using Infrastructure as Code (e.g., Ansible, Terraform) across bare metal, VMs, and GPU-accelerated Kubernetes clusters.
Optimize GPU inference environments with technologies such as NVIDIA Triton Inference Server and advanced model optimization techniques to enable production-grade model serving.
Hands-on experience with MLOps orchestration tools including Kubeflow, MLflow, or similar and Infrastructure as Code tools like Terraform or Ansible.
Advanced proficiency in Python including libraries pandas and NumPy; experience with PyTorch focused on distributed training and model handling.
Strong working knowledge of GPU computing (CUDA), NVIDIA Triton Inference Server, Kubernetes for GPU resource management, and persistent container storage.
Work Experience Required: Not explicitly mentioned in the JD. Location Requirement: Must work primarily from the specified office location as the role is in-office.
Experienced in architecting and automating complex, scalable MLOps pipelines in production environments leveraging Pure Storage high-performance data platforms.
Deep technical skillset in GPU-accelerated AI/ML workloads with proven ability to design optimized inference solutions for large models.
Capable of collaborating with data scientists and product management to influence AI platform integration strategies and drive ecosystem adoption.