Senior Solutions Engineer - MLOps
Pure StorageBangalore, India
Full Time
Posted December 2, 2025
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Job Description
A Senior MLOps Solutions Engineer responsible for designing and implementing high-scale, enterprise AI/ML workflows, integrating storage and orchestration platforms, and enabling production AI solutions through automation, optimization, and collaboration with cross-functional teams.
Key Responsibilities
- Design and automate end-to-end MLOps pipelines using CI/CD tools and orchestration platforms
- Develop high-performance AI/ML reference architectures and deployment models with Infrastructure as Code
- Optimize and operationalize GPU inference solutions for high-throughput, low-latency model serving
- Create automated, GPU-enabled MLOps lab environments and technical documentation to support sales and ecosystem adoption
- Collaborate with data scientists and product management to influence AI platform integration strategies and product roadmaps
Requirements
- Experience designing, building, and automating MLOps workflows using orchestration tools such as Kubeflow, MLflow, Vertex AI, and SageMaker.
- Proficiency with Infrastructure as Code (IaC) tools such as Terraform or Ansible.
- Deep expertise in Python for Data Science and MLOps, including libraries like pandas and NumPy.
- Demonstrated experience with Deep Learning frameworks, particularly PyTorch, focusing on distributed training and model handling.
- Hands-on experience designing, building, and automating MLOps pipelines using orchestration tools such as Kubeflow, MLflow, Vertex AI, and SageMaker.
- Experience integrating high-performance storage platforms such as Pure Storage s FlashBlade, FlashArray, and Portworx for data ingestion, training, and inference.
- Experience creating validated, repeatable deployment models using Infrastructure as Code for AI/ML environments spanning bare metal, virtual machines, and GPU-accelerated Kubernetes clusters.
- Knowledge of GPU-accelerated computing principles, including CUDA, and experience with NVIDIA Triton Inference Server.
- Expertise in container orchestration with Kubernetes for GPU resource management, scheduling, and persistent storage for containers.
- 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.
- Experience architecting and implementing solutions for high-throughput, low-latency model serving, utilizing technologies like NVIDIA Triton Inference Server, and optimization techniques such as quantization, model sharding (e.g., DeepSpeed Megatron-LM), and dynamic batching for large models (LLMs).
- Ability to develop automated, GPU-enabled MLOps lab environments, create high-quality technical documentation, and deliver live demonstrations to enable sales, field engineering teams, and strategic partners.
- Strong collaboration skills to work closely with Data Scientists and Product Management to influence technical strategy for AI platform integrations and contribute to future product roadmaps related to accelerated compute and high-performance storage.
- Willingness to work from the OFFICE_LOCATION office in compliance with company 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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