Machine Learning Specialist

The aim is to provide a robust and lean user interaction capabilities assisting external users to find easier funding opportunities on EU level, featuring a personalised interface and more powerful search capabilities.

• Architect, set up and manage an MLOps infrastructure;
• Manage the lifecycle of ML models, including version control, build automation, CI, testing, monitoring, automation, disaster recovery;
• Training of custom machine learning / deep learning models based on structured and unstructured data;
• Selecting features, building and optimizing classifiers using machine learning techniques;
• Define data controls and implement actions to ensure data quality and integrity;
• Creating automated anomaly detection systems and constant tracking of its performance;
• Processing, cleansing, and verifying the integrity of data used for analysis;
• Design the IT architecture for solutions in the NLP / ML / AI fields, and coordinate its implementation considering master- and meta-data management concepts;
• Provision of security studies, security assessments or other security matters associated with information system projects;
• Contributing for the analysis of data management vision, strategy and policy and derive the IT requirements;
• Contributing to the design of the IT architecture considering master- and meta-data management concepts.

Requirements

• Master's level degree in the IT field
• At least 10 years of experience in a relevant field.
• Good knowledge of managing a MLOps / Big Data infrastructure.

• Experience with managing the full lifecycle of ML models.
• Good knowledge of
  • application containerization, virtualization, orchestration.
  • TensorFlow (TFX), PyTorch, Kubeflow or equivalents.
  • AWS and/or Azure.
  • Unix and Bash.
  • natural language processing systems lifecycle and agile software development methodologies.
  • quality assurance and quality control for machine translation (MT) and experience with MT quality procedures, testing methodologies and tools, such as automatic quality metrics (BLEU scores and similar) and human evaluation of MT quality.
• Knowledge of query languages, such as SQL, Hive, Pig, etc and with information extraction.
• Knowledge of NoSQL databases.
• Experience in the field of corpus-based linguistics.
• Experience with alignment models and classification methods.
• Experience with data analytics over big datasets, non-structured databases as well as data lakes.
• Very good knowledge of cloud solutions for datawarehouse, data lakes and business intelligence solutions.
• Ability to understand, speak and write English C1, French C1 will be an advantage