About This Role
We are seeking a hands-on, customer-facing technical expert to lead the deployment of AI/ML workloads on high-performance GPU infrastructure. The ideal candidate will have deep technical expertise in Kubernetes and MLOps.
This role requires strong communication skills to navigate stakeholder conversations, gather requirements, and lead technical engagements.
Key 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: Design infrastructure that balances performance, scalability, and efficiency.
* Cross-Cloud Translation: Help customers migrate and adapt workloads across AWS, Azure, and GCP.
* Technical Storytelling: Conduct workshops, live demos, and solution reviews.
Requirements:
* Deep Kubernetes Expertise: 3-5 years building and deploying containerized workloads.
* 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.
* Customer-Facing Technical Confidence: Able to navigate stakeholder conversations, gather requirements, lead technical engagements, and support customers in both pre- and post-sales environments.
Benefits:
We offer a competitive benefits package designed to support financial security, health, and overall well-being, including pension contributions, private health insurance, income protection, life assurance and more.
Nice to Have:
* 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 ? Crusoe transitions).
* Content contributions (tech talks, blogs, public case studies).
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