Your tasks

Strategic consulting

• From the perspective of a Machine Learning Engineer DevOps/MLOps, you will advise and support our customers in the design of 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 on premise as part of ML projects.

Data Engineering

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

Operationalization

• You integrate ML models into the customer’s ML infrastructure and ensure their operation.
• You are responsible for the reliability of the ML models and provide our customers with appropriate monitoring (ML infrastructure/applications/specialist monitoring).

We offer

• Travel time is recognized 100% as working time, home office, compensatory time off and state-of-the-art workplaces are a matter of course for us.
• Great freedom to act on one’s own responsibility and to develop and implement one’s own ideas in order to actively help shape the direct working environment and the company.
• A distinctive corporate culture of innovation and a wide range of opportunities for personal development and training.
• An open and friendly corporate culture with nice and helpful colleagues.
• Short, personal and efficient communication channels at management and board level ensure fast and efficient work.

Your qualifications

• You 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 have a good overview of current technological developments and tools in Machine Learning.
• You have very good knowledge in:
  • Python: pandas, scikit-learn, PyTorch, TensorFlow.
  • Machine Learning: NLP, supervised and unsupervised learning, time series analysis,
  • Optional: Deep Learning
• Good knowledge of one of each of the following technologies:
  • Operationalization: mlFlow, Kubeflow
  • Infrastructure: Terraform, Ansible
  • Monitoring: Elasticsearch, Prometheus, Splunk
• Good 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
• The flexibility and mobility required in a consulting environment
  and mobility is a matter of course for you.