Machine Learning Engineers typically help data scientists 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.
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
Requirements - as many of
Containerization
AI platforms & IDEs
CI/CD
Code, model & data versioning
Cloud computing services
Relational databases
Swagger/Schema definition
If you are collaborative, motivated and willing to work in a team environment then please send me your CV for initial review.