Solutions Engineer
This customer-facing Solutions Engineer leads enterprise AI/ML workload deployments on GPU infrastructure, from proof of concept through optimization. The role requires strong Kubernetes, MLOps, Linux, and multi-cloud expertise, along with the ability to guide technical stakeholders and translate customer needs to engineering teams.
About the job
Responsibilities
- Lead technical onboarding and deployment of complex AI/ML workloads for strategic enterprise customers, owning proofs of concept through post-sales optimization.
- Architect and deploy machine-learning workloads using Kubernetes-based stacks such as Ray and Kubeflow.
- Design infrastructure that balances performance, scalability, and efficiency.
- Deploy and optimize AI/ML workloads directly on Crusoe infrastructure, including at the container and hardware levels.
- Help customers migrate and adapt workloads across AWS, Azure, and Google Cloud, explaining cloud-native and Crusoe-native tradeoffs.
- Conduct workshops, live demos, and solution reviews.
- Contribute to case studies, solution briefs, and blog posts.
- Relay customer feedback to Engineering and Product teams.
- Gather requirements, lead technical engagements, and support customers in pre- and post-sales environments.
- Troubleshoot infrastructure issues through Linux command-line tools.
Requirements
- 3–5 years of experience building and deploying containerized workloads.
- Deep Kubernetes expertise, including Helm, Terraform, Docker, and multi-node orchestration.
- Demonstrated experience deploying ML frameworks such as Ray, MLflow, and Airflow on Kubernetes for inference and model-training workflows.
- Knowledge of compute, storage, networking, and scaling in AWS, Google Cloud, or Azure.
- Strong customer-facing technical communication and stakeholder-management skills.
- Strong Linux and CLI proficiency.
- Ability to collaborate with Engineering, Product, and Sales.
- Ability to pass a background check.
Nice to have
- Experience with Ray, Kubeflow, or other distributed ML orchestration platforms.
- Exposure to Slurm, with a primary focus on containerized MLOps.
- Multi-cloud deployment or migration experience, especially AWS-to-Crusoe transitions.
- Technical talks, blog posts, or public case studies.
Compensation and benefits
- Competitive benefits package including pension contributions, private health and dental insurance, income protection, and life assurance.
- Compensation may be paid as salary or hourly and will be determined by education, experience, knowledge, skills, abilities, internal equity, and market data.
Skills
Kubernetes, MLOps, Ray, Kubeflow, Helm, Terraform, Docker, MLflow, Airflow, AWS, GCP, Microsoft Azure, Linux, Slurm, Cli
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