Our client is a cloud technology company driving the next generation of AI infrastructure. They empower organizations to build and scale AI/ML solutions without the need for massive in-house teams or heavy upfront infrastructure costs. Their global team of engineers and researchers works at the forefront of GPU cloud computing, supporting businesses across industries to solve complex, real-world problems.

The company operates with a flat structure, minimal bureaucracy, and a strong focus on speed, ownership, and technical excellence. Engineers work directly with customers to design scalable solutions and influence product direction, creating real impact in how modern AI applications are built and deployed

They are seeking an experienced Cloud Solutions Architect with deep expertise in cloud infrastructure and MLOps. In this role, you will act as a trusted technical advisor for customers, designing and implementing scalableAI/ML solutions powered by modern GPU cloud environments.

You will help clients optimize their pipelines, run proof-of-concepts, and provide guidance on best practices, while also collaborating internally to shape product and customer success strategies

• Act as a trusted advisor to customers, conducting workshops, presentations, and training on GPU cloud technologies
• Translate business requirements into scalable, cloud-native solution architectures
• Design and document infrastructure-as-code (IaC) deployments and technical guides in collaboration with support engineers and technical writers
• Optimize customer ML pipelines for performance, scalability, and cost efficiency• Serve as the key point of expertise on customer use cases for product, engineering, and marketing teams

• 5/10+ years of experience in cloud computing roles (solutions architect, systems engineer, developer,etc.)
• Strong hands-on experience with IaC (Terraform/Ansible), Kubernetes, and Python
• Solid understanding of GPU computing for ML training/inference and GPU software stacks (CUDA, OpenCL)
• Excellent communication and presentation skills
• Strong customer-facing and problem-solving mindset.

Bonus Points For:
• Experience with HPC/ML orchestration frameworks (Slurm, Kubeflow)
• Hands-on experience with deep learning frameworks (TensorFlow, PyTorch)
• Familiarity with leading cloud ML ecosystems (AWS, Azure, Google, NVIDIA)

Compensation: 180K–280K OTE (base + performance-based bonus) / RSUs. 180K Base = max