ML Ops Specialist

Job Description

As a Machine Learning Ops (MLOps) engineer, you will be responsible for building and maintaining the platform and infrastructure of Innovatrics R&D department. You will lead initiatives geared towards making the ML Engineers and Applied Scientists in Innovatrics more productive. You will make improvements and extend the underlying infrastructure that powers the ML teams, thus simplifying the development and deployment cycles of ML models. You will help establish best practices for the ML pipeline and partner with other infrastructure ops teams to help champion them across the company.

Responsibilities

• Close collaboration with R&D / ITO teams
• ML Ops architecture design

1. Kubernetes cluster (+ customized schedulers) onprem / cloud ML processes execution workflows
2. Design secure access workflows for data and git repositories
3. Integration with Git / GitLab / DVC / Docker
4. Automation support (e.g. manage shared CI pipelines, kubernetes job commits, releasing process automation)
5. Scaling of ML operations  

• ML infrastructure administration

1. Multi GPU Servers
2. Kubernetes Cluster
3. Dataset storage
4. Create automatized monitoring of performance / bottlenecks (Server resources, CPU / GPU / Memory / storage, Kubernetes cluster, S3 storage, Network)

• Maintenance (Update drivers / SW / security certificates, Update base docker images for all purposes)

Candidate requirements

• 3+ years of experience with DevOps. Experience with ML infrastructure is a big plus 
• Experience with onprem Kubernetes and ML CI/CD workflows
• Experience with AWS or other public cloud platforms (GCP, Azure, etc.)
• Experience with distributed data storages
• Experience with coding in Python (preferred) or other languages like Java, C#, etc.
• Responsible approach to work, ability to work independently
• Willingness to help others and share knowledge
• Good analytical skills and thinking
• Good command of the English language

Required education

• University education (Bachelor's degree)
• University education (Master's degree)
• Postgraduate (Doctorate)