Solutions Engineer

Crusoe Energy Systems; Ireland; On-site; EUR 65,000 - 85,000; Full time

Benefits offered by this job

Comprehensive health benefits

Paid time off

401(k) matching

Mental wellness resources

Job summary

Crusoe Energy Systems is looking for a Solutions Engineer in Ireland to work with enterprise customers deploying AI/ML workloads on high-performance GPU infrastructure. This role requires deep technical expertise in Kubernetes and MLOps, along with strong technical storytelling abilities.

Your responsibilities will include leading onboarding, optimizing post-sale workloads, and ensuring successful deployments across cloud platforms. You will be part of a collaborative environment focused on customer success, aiming to improve Crusoe’s platform based on real-world implementations.

Qualifications

• 3-5 years of experience in building and deploying containerized workloads.
• Proficient in Linux environments and CLI troubleshooting.
• Experience deploying ML frameworks on Kubernetes.

Responsibilities

• Lead technical onboarding and deployment of AI/ML workloads.
• Optimize workloads post-sale and serve as a technical voice.
• Help customers migrate workloads across cloud platforms.

Skills

Kubernetes

MLOps

Linux

Cloud platforms (AWS, GCP, Azure)

Containerization technologies (Docker, Helm, Terraform)

Multi-cloud deployment

Technical storytelling

Tools

Ray

MLflow

Airflow

Job description

Crusoe Cloud is seeking a Solutions Engineer to work closely with our most strategic enterprise customers deploying AI/ML workloads on Crusoe’s high-performance GPU infrastructure.

Responsibilities

• Crusoe Cloud is seeking a Solutions Engineer to work closely with our most strategic enterprise customers deploying AI/ML workloads on Crusoe’s high-performance GPU infrastructure
• This is a hands‑on, customer‑facing role requiring deep technical expertise in Kubernetes, MLOps, and cloud infrastructure
• You’ll guide customers through end‑to‑end deployment—owning the PoC process, optimizing workloads post‑sale, and serving as a critical technical voice between our customers and engineering teams
• 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

Benefits

• Health & wellbeing: Comprehensive health benefits designed to support your overall wellness
• Time away: Paid time off for vacations, family bonding, and unexpected needs
• 401(k) match: Build your financial future with our 401(k) matching program
• Mental wellness: Resources and support for your emotional wellbeing and navigating life’s challenges

Qualifications

• Ideal candidates are passionate about AI infrastructure, fluent in containerized environments, and confident translating workloads across cloud platforms
• Strong Linux and CLI Proficiency: Comfortable operating in Linux environments and troubleshooting infrastructure issues via CLI
• Customer‑Facing Technical Confidence: Able to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in both pre‑ and post‑sales environments
• 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
• Collaborative Energy: Strong communication skills and eagerness to partner cross‑functionally with Engineering, Product, and Sales to make customers successful
• Deep Kubernetes Expertise: 3‑5 years building and deploying containerized workloads. Experience with Helm, Terraform, Docker, and multi‑node orchestration a must
• Experience with Ray, Kubeflow, or other distributed ML orchestration platforms
• Exposure to Slurm, but with a primary focus on containerized MLOps over traditional HPC
• Multi‑cloud deployment or migration experience (especially AWS to Crusoe transitions)
• Content contributions (tech talks, blogs, public case studies)
• Must be able to pass a background check
• Embody the Company values