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Are you an experienced Cloud Engineer that wants to jump into Machine Learning and Deep Learning? We are looking for a Cloud Architect that wants to productionalize machine learning models and become a Lead MLOps Architect.
At ReBatch we use MLOps principles to develop and deploy machine and deep-learning models in production
MLOps covers three fields: consultancy, data science and operationalization of machine learning models. You will take the lead in operationalization of machine learning models. The candidate will not only be at the forefront of MLOps, but will also have a big influence at scaling the MLOps team.
At ReBatch we want to be at the forefront of MLOps in the Benelux and we are looking for an experienced Cloud Architect who can make this happen.
• Possibility to learn Machine Learning, Artificial Intelligence and Deep Learning (train your own Neural net!)
• Play around with big clusters of CPU’s, GPU’s and TPU’s and deploy State of the Art Artificial Intelligence models
• Opportunity to setup and lead a team of MLOps and Data Engineers
• Competitive salary and benefits such as: company car, hospital insurance, group insurance and a laptop
• You have a bachelor or master’s degree in computer science
• You have roughly five years’ experience
• You have experience with Docker and orchestration platforms such as Kubernetes
• You have experience in DevOps methodologies like CI/CD pipelines,Infrastructructure as Code and Automated testing
• You have experience with GCP, AWS or Azure
• You have experience with monitoring, eg: Grafana or Prometheus
• Python is familiar territory
• SQL holds no secrets for you
• Experience with developing and using API’s
• You can mentor junior DevOps or MLOps Engineers
• You are interested in learning Machine Learning
• Experience in Data Science, Tensorflow, PyTorch or Keras is a plus, but not necessary. We will teach you the inner workings of Machine Learning and AI!
• Setup architecture design and development of machine learning pipelines and integration into enterprise systems
• Setup and maintain GKE clusters to train big machine learning models with huge datasets
• Setup CI/CD pipelines for Machine Learning models
• Monitoring performance of production systems
• Build APIs around Machine Learnings models and optimize predictions
• Interact with Data Scientists to optimize machine learning models
• Guiding junior DevOps or MLOps Engineers