Senior Solutions Engineer - MLOps
Pure StorageBangalore, India
Full Time
Posted December 12, 2025
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Job Description
A Senior MLOps Solutions Engineer responsible for designing, building, and automating high-scale AI/ML workflows, integrating storage and orchestration platforms, and enabling production AI solutions through innovative MLOps practices and high-performance infrastructure.
Key Responsibilities
- Design and automate end-to-end MLOps pipelines using CI/CD tools and orchestration platforms
- Develop validated deployment models with Infrastructure as Code for various AI/ML environments
- Architect and implement high-performance GPU inference solutions for low-latency model serving
- Create automated, GPU-enabled MLOps lab environments and technical documentation to support sales and partners
- Collaborate with data scientists and product management to influence AI platform strategies and product roadmaps
Requirements
- Design and Automate MLOps Pipelines using CI/CD tools such as Git and Jenkins, and orchestration platforms like MLflow and Kubeflow, integrating Pure Storage s FlashBlade, FlashArray, and Portworx for data ingestion, training, and inference.
- Lead the development of end-to-end MLOps workflows using orchestration tools such as MLflow and Kubeflow, and integrate high-performance data storage solutions including Pure Storage s FlashBlade, FlashArray, and Portworx.
- Create validated, repeatable deployment models using Infrastructure as Code (IaC) tools such as Ansible and Terraform for AI/ML environments spanning bare metal, virtual machines, and GPU-accelerated Kubernetes clusters, ensuring optimal performance for distributed training.
- Design and implement solutions for high-throughput, low-latency GPU inference, utilizing NVIDIA Triton Inference Server, and advanced optimization techniques such as quantization, model sharding (e.g., DeepSpeed Megatron-LM), and dynamic batching for large models (LLMs).
- Develop automated, GPU-enabled MLOps lab environments, produce high-quality technical documentation, and deliver live demonstrations to enable global sales, field engineering teams, and strategic partners on new AI integrations.
- Collaborate with Data Scientists and Product Management to influence the technical strategy for AI platform integrations and contribute to future product roadmaps related to accelerated compute and high-performance storage requirements.
- Possess hands-on experience designing, building, and automating MLOps workflows using orchestration tools such as Kubeflow, MLflow, Vertex AI, and SageMaker, with proficiency in Infrastructure as Code (IaC) tools like Terraform or Ansible.
- Have advanced proficiency in Python for Data Science and MLOps, including libraries such as pandas and NumPy, and demonstrated experience with Deep Learning frameworks, particularly PyTorch, focusing on distributed training and model handling.
- Possess expertise in GPU-Accelerated Computing, including practical knowledge of CUDA, NVIDIA Triton Inference Server, and Kubernetes for GPU resource management, scheduling, and persistent storage for containers.
- Have a solid understanding of high-performance data center infrastructure, including high-speed networking technologies such as RoCE and 100GbE, and storage platforms optimized for high-throughput, low-latency AI/ML workloads, such as high-performance S3 and parallel file systems.
- Be willing to work from the OFFICE_LOCATION office in compliance with Pure s policies, unless on PTO, work travel, or other approved leave.
Benefits & Perks
Flexible time off
Wellness resources
Company-sponsored team events
Recognition as Fortune's Best Large Workplaces in the Bay Area
Recognition as Fortune's Best Workplaces for Millennials
Certified as a Great Place to Work
Accommodations for candidates with disabilities
Inclusive and diverse work environment
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