Principal Solutions Architect (Remote)
Doghouse Recruitment
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About the role
Role Overview
Principal Solutions Architect focused on AI/ML cloud infrastructure and MLOps. You will serve as a trusted technical advisor to customers, helping them design and implement scalable AI/ML solutions on modern GPU cloud environments.
Responsibilities
- Act as a trusted advisor for customers through 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 subject-matter expert on customer use cases for product, engineering, and marketing teams
- Run proof-of-concepts and provide guidance on best practices
Requirements
- 5–10+ years of experience in cloud computing roles (solutions architect, systems engineer, developer, etc.)
- Hands-on experience with IaC using Terraform and/or Ansible
- Solid experience with Kubernetes and Python
- Strong understanding of GPU computing for ML training/inference, including GPU software stacks such as CUDA and OpenCL
- Excellent communication and presentation skills
- Strong customer-facing and problem-solving mindset
Bonus Points
- Experience with HPC/ML orchestration frameworks (e.g., Slurm, Kubeflow)
- Hands-on experience with deep learning frameworks (TensorFlow, PyTorch)
- Familiarity with major cloud ML ecosystems (AWS, Azure, Google Cloud, NVIDIA)
Compensation & Benefits
- Up to $480K OTE (base + performance-based) plus equity/RSUs
- 100% employer-paid medical, dental, and vision for employees and families
- 401(k) with up to 4% employer match (immediate vesting)
- 20 weeks paid parental leave (primary caregivers), 12 weeks (secondary)
About Doghouse Recruitment
The company is a cloud technology provider focused on next-generation AI infrastructure. It helps organizations build and scale AI/ML solutions using GPU cloud computing without large in-house teams or heavy upfront infrastructure costs. With a flat, fast-moving engineering culture, teams work directly with customers to design scalable AI solutions and influence product direction.
Scraped 4/2/2026