Senior Solutions Architect II, AI/ML

DigitalOcean is looking for a Senior Solutions Architect with expertise in cloud infrastructure and AI/ML to support the success and retention of its high-value customers. You will work directly with Customer Success, Sales, and Support to retain and grow DigitalOcean's largest and fastest growing customers, acting as the technical subject matter expert on the product portfolio, advising on best practices, and guiding customers to the optimal solution for their business objectives. You will perform architecture reviews, proof of concepts, and demos, and help large customers expand their workloads on DigitalOcean.

Key Responsibilities:

• Develop deep expertise in the DigitalOcean product portfolio, the evolving cloud landscape, and AI/ML platforms.
• Design and review architectures tailored to specific business use cases, ensuring optimal solutions for customers.
• Offer personalized onboarding assistance and guide customers through their cloud journey, resolving technical blockers.
• Identify opportunities for cost reduction and performance improvement, helping customers make data-driven cloud optimization decisions.
• Conduct technical consultation sessions, workshops, and training to empower customers in managing cloud infrastructure.
• Optimize GPU workloads using CUDA or TensorRT and build scalable AI applications such as chatbots, inference services, and recommendation systems using Kubernetes and NFS.
• Advocate for customers by providing feedback to product teams, overcoming adoption blockers, and driving new feature development.
• Partner with Growth Account Managers and Customer Success Managers on success plans that ensure retention, expansion, and satisfaction.

Qualifications:

• Proven professional experience with cloud infrastructure and AI/ML platforms, ideally in a post-sales or technical consultant role.
• Expertise in AI/ML frameworks (TensorFlow, PyTorch) and familiarity with platforms like Hugging Face.
• Experience deploying and fine-tuning LLMs (DeepSeek, Llama, Claude, GPT-4) and GenAI models, including hands-on use of vLLM and quantization methods (INT4, INT8, FP8) for efficient model deployment.
• Deep knowledge of Linux, distributed systems, Kubernetes, NFS, object storage, and GPU optimization techniques (CUDA, TensorRT).
• Programming experience building AI-powered applications, plus proficiency in DevOps tools like Docker, Terraform, and CI/CD pipelines.
• Strong communication skills, with the ability to explain technical AI/ML concepts in clear and concise terms to multiple stakeholders.
• Extra credit: cloud certifications (AWS/GCP/Oracle/Azure), NVIDIA GPU and AI/ML certifications, and Kubernetes certifications (CKA/CKE).