Solutions Engineer
You will lead technical onboarding and deployment of complex AI and machine learning workloads for strategic enterprise customers. You will own proofs of concept through post-sale optimization, architect Kubernetes and MLOps infrastructure, optimize workloads, translate deployments across cloud platforms, conduct workshops and demos, and relay customer feedback to engineering and product teams.
Responsibilities
- Lead technical onboarding and deployment of complex AI and machine learning workloads
- Own the proof-of-concept process through post-sale optimization
- Architect and deploy ML workloads using Kubernetes-based stacks
- Balance infrastructure performance, scalability, and efficiency
- Deploy and optimize workloads directly on infrastructure
- Help customers migrate and adapt workloads across AWS, Azure, and GCP
- Conduct workshops, live demos, and solution reviews
- Contribute to case studies, solution briefs, and blog posts
- Relay customer feedback to engineering and product teams
Requirements
- 3-5 years building and deploying containerized workloads
- Experience with Helm, Terraform, Docker, and multi-node orchestration
- Demonstrated success deploying Ray, MLflow, or Airflow on Kubernetes
- Experience with inference and model training workflows
- Knowledge of compute, storage, networking, and scaling in AWS, GCP, or Azure
- Experience translating workloads across cloud platforms
- Ability to navigate stakeholder conversations, gather requirements, and lead technical engagements
- Strong Linux and command-line proficiency
- Ability to troubleshoot infrastructure issues via CLI
- Experience with distributed ML orchestration platforms is a bonus
- Exposure to Slurm is a bonus
- Multi-cloud deployment or migration experience is a bonus
- Ability to pass a background check
Benefits
- Pension contributions
- Private health insurance
- Dental insurance
- Income protection
- Life assurance