Company Description
Machine Learning Engineers promote the adoption of best standards in industrial code development across the ML&AI community. They do so by developing ML pipelines that are production-ready by design or by integrating existing ML solutions into industrial pipelines.
Job Description
They participate in the development, deployment and monitoring of AI services, which means they contribute to data quality checks, data flow design, the design of the models themselves and their overall integration into the production environment.
ML Engineers are meant to facilitate the communication between AI & Analytics teams and IT production with regards to the deployment of ML models, ensuring that models put in production are equipped with the appropriate data pipelines and monitoring.
ML Engineers contribute to Machine Learning projects by:
- Working with the Data Scientists to define and develop the target solution with production constraints in mind. This allows to select the correct run infrastructure and serving model (e.g. data ingestion scheme, API synchronicity, …) to address the business requirements (real-time responses, processing volumetry, …)
- Contributing to the automation of the different elements of the ML pipeline in order to integrate and deploy them in the production environment (e.g. building Docker/VM images, prepare unitary, regression and integration tests, …)
- Supporting Data Scientists on the usage of the existing industrial solutions available to build and monitor AI services (i.e. the CI/CD tools)
- Supporting IT Production on the parameterization of the target environment
Ensuring that the model runs without errors, is retrained if needed (incl. automatically) and is monitored both from the IT and the business perspective.
Additional Information
Technical Experience Required:
- Containerization / virtualisation
- AI platforms & IDEs
- CI/CD (gitlab-ci)
- Code, model & data versioning
- Python
- Usage of package manging tools and experience in dependency management
- postgresql
- Experience with integration using different technologies (distributes/mainframe) and infra components
- Model compression techniques
- ELT / ETL tools
- Big data tools (Spark)
- Data flow processing
- Data visualization tools
