Senior Solutions Engineer at Crusoe

Dublin, County Dublin, Ireland

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

AI, Cloud ComputingIndustries

Requirements

Deep Kubernetes Expertise: 3-5 years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi-node orchestration
MLOps Deployment Experience: Demonstrated success deploying ML frameworks (e.g., Ray, MLflow, Airflow) on Kubernetes—especially for inference and model training workflows
Hands-on Cloud Infrastructure Knowledge: Familiarity with compute, storage, networking, and scaling in AWS, GCP, or Azure. Experience translating workloads across clouds is highly desirable
Customer-Facing Technical Confidence: Able to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in both pre- and post-sales environments
Strong Linux and CLI Proficiency: Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI
Collaborative Energy: Strong communication skills and eagerness to partner cross-functionally with Engineering, Product, and Sales to make customers successful
Must be able to pass a background check
Bonus Points
Experience with Ray, Kubeflow, or other distributed ML orchestration platforms
Exposure to Slurm, but with a primary focus on containerized MLOps over traditional HPCM
Multi-cloud deployment or migration experience (especially AWS ➝ Crusoe transitions)
Content contributions (tech talks, blogs, public case studies)

Responsibilities

Customer Enablement: Lead technical onboarding and deployment of complex AI/ML workloads with strategic enterprise customers—owning the PoC through to post-sales optimization
Kubernetes + MLOps Focus: Architect and deploy ML workloads using Kubernetes-based stacks (e.g., Ray, Kubeflow). Design infrastructure that balances performance, scalability, and efficiency
Infrastructure-Centric Thinking: Go beyond abstracted services—deploy and optimize AI/ML workloads directly on Crusoe infrastructure. Ensure performance at the container and hardware level
Cross-Cloud Translation: Help customers migrate and adapt workloads across AWS, Azure, and GCP. Understand and explain the tradeoffs between cloud-native and Crusoe-native approaches
Technical Storytelling: Conduct workshops, live demos, and solution reviews. Contribute to case studies, solution briefs, and blog posts that highlight real-world customer success
Voice of the Customer: Relay feedback to internal engineering and product teams to continuously improve Crusoe’s platform based on real-world implementation experience

Skills

Kubernetes

MLOps

Ray

Kubeflow

GPU

AI/ML

Cloud Infrastructure

Containerized Environments

Crusoe

Utilizes wasted energy for computing power

About Crusoe

Crusoe Energy Systems Inc. provides digital infrastructure that focuses on using wasted, stranded, or clean energy sources to power high-performance computing and artificial intelligence. The company helps clients in the technology and energy sectors by offering scalable computing solutions that aim to reduce greenhouse gas emissions and support the transition to cleaner energy. Crusoe's approach involves converting excess natural gas and renewable energy into computing power, which allows them to maximize resource efficiency while minimizing environmental impact. Unlike many competitors, Crusoe specifically targets the intersection of energy and technology, generating revenue by supplying computing resources to enterprises that need significant computational power for applications like AI and machine learning, along with providing technical support.

Denver, ColoradoHeadquarters

2018Year Founded

$1,082.2MTotal Funding

SERIES_DCompany Stage

Energy, AI & Machine LearningIndustries

201-500Employees