Your role

Strategic consulting

• From the perspective of a Machine Learning Engineer DevOps/MLOps, you will advise and support our customers with designing their ML infrastructure based on IT infrastructure.
• As part of a highly skilled team, you will ensure that scalable IT infrastructures are created in the cloud or locally as part of ML projects.

Data Engineering

• In this respect, you will build robust data pipelines using data from our customers’ existing systems.

Operationalization

• You will integrate ML models into the customer’s ML infrastructure and ensure that they function correctly.
• You will be responsible for the reliability of the ML models and provide our customers with appropriate monitoring (ML infrastructure/applications/specialist monitoring).

What we offer

• Travel time is 100% recognized as working time, while homeworking, time off in lieu and state-of-the-art workplaces are a matter of course for us.
• A large degree of freedom to act on your own initiative and to develop and implement your own ideas in order actively to help shape the direct work environment and the company.
• A strong corporate culture of innovation and a wide range of opportunities for personal development and training.
• An open and friendly corporate culture with friendly and helpful colleagues.
• Short, personal and efficient communication channels at management and board level ensure fast and efficient work.

Your qualifications

• You should have at least 3-5 years of relevant work experience, ideally in the role of a Machine Learning Expert/Machine Learning Scientist or DevOps in a ML context.
• You should have a good overview of current technological developments and tools in Machine Learning.
• You will ideally have a very good understanding of:
  • Python: pandas, scikit-learn, PyTorch, TensorFlow.
  • Machine Learning: NLP, supervised and unsupervised learning, time series analysis,
  • Optional: Deep Learning
• Well-rounded understanding of one of each of the following technologies: Good knowledge of one of each of the following technologies:
  • Operationalization: mlFlow, Kubeflow
  • Infrastructure: Terraform, Ansible
  • Monitoring: Elasticsearch, Prometheus, Splunk
• Good working knowledge of:
  • Data Engineering: Spark, Kafka, Airflow, MS SQL
  • Cloud Platforms (in at least one cloud platform): Azure, AWS (SageMaker), GCP (AI Platform).
  • Container Technologies: Docker, Kubernetes
    (OpenShift), microservice architectures
• You should have the flexibility and mobility necessary in a consulting environment.