# Solutions Engineer

**Company:** [Crusoe](https://hotfix.jobs/companies/crusoe)
**Location:** Dublin, Ireland
**Role:** Solutions Architecture
**Experience:** 3+ years
**Skills:** Kubernetes, MLOps, Ray, Kubeflow, Helm, Terraform, Docker, MLflow, Airflow, AWS, GCP, Microsoft Azure, Linux, Slurm, Cli
**Posted:** 2026-05-29

> 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.

## Job Description

## 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.

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**Canonical:** https://hotfix.jobs/jobs/1cb4e3d1-3b19-4f36-953c-433f5ce2499c