MTS - AI / MLOps
Core
Architect and build high-scale, enterprise-grade AI/ML solutions by integrating Pure Storage platforms with the open-source MLOps ecosystem to operationalize the complete machine learning lifecycle.
Role type
Senior MLOps Solutions Engineer
Builds
High-performance AI/ML reference architectures, automated MLOps pipelines, and GPU-enabled lab environments for sales and partners.
Domain
Data storage infrastructure, AI/ML operations, GPU computing
Deliverable
production ML models
Required skills
MLOps pipeline design, Infrastructure as Code (Terraform, Ansible), Python (pandas, NumPy), Deep Learning frameworks (PyTorch), GPU computing (CUDA), Kubernetes, NVIDIA Triton Inference Server, high-performance storage systems
Preferred skills
Experience with Kubeflow, MLflow, Vertex AI, SageMaker, distributed training, model optimization (quantization, sharding)
Technologies
Pure Storage FlashBlade, FlashArray, Portworx, Git, Jenkins, Kubeflow, MLflow, Ray, Ansible, Terraform, NVIDIA Triton, PyTorch, CUDA, Kubernetes
Responsibilities
Design and automate end-to-end MLOps workflows integrating storage platforms; Build validated deployment models for AI/ML environments spanning bare metal to GPU clusters; Architect solutions for high-throughput, low-latency model serving; Develop automated lab environments and technical documentation to enable sales and partners; Collaborate with Data Scientists and Product Management to influence technical strategy for AI platform integrations.
Seniority
Senior, hands-on IC