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The ML Ops Specialist implements, maintains and evolves the platform to enable the development and deployment of machine learning models for the Quality Foundation Technology Group (QFTG). He/she works closely and collaboratively with DevOps, developers and data scientists.

The ML Ops Specialist provides solutions that enable :

  • Model production (training)

  • Set up the software environment (any tools needed to use the models)

  • Optimize infrastructure and deployment in production (collaboration with Ops and developers)

  • Improve quality (quality assurance)

Responsibilities:

The main and usual duties of this job are:

  • Deploy and operationalize ML models

  • Contribute to the industrialization of products using Generative AI and/or Machine learning

  • Set up software infrastructure

  • Implement model validation

  • Contribute to the creation of new models, with a focus on their production.

  • Contribute to the quality of models and data generated from them (e.g., set up a QA-friendly environment).

  • Identify problems and determine quantitative approaches and methods to develop solutions;

  • Develop tools to solve problems

  • Document and define projects including data collection and processing, state-of-the-art approaches, final algorithm, detailed results and analytical criteria;

  • Collaborate with colleagues (Developers, Dev Ops, Research Scientists) and managers internally and externally to share relevant complex information;

  • Contribute to the identification of approaches and the development of new or improved technical tools;

  • Act as a consultant to guide technologies and/or advise on proof-of-concepts for forecasting/prescription and AI projects;

  • Mentor less experienced colleagues;

  • Perform all other related tasks.

Qualifications

Education:

  • Bachelor's or Master's degree in computer science, computer or software engineering or equivalent.

Relevant experience:

  • 3 to 5 years of complex experience and in-depth expertise related to the position.

Skills and knowledge:

  • Experience with ML frameworks

  • Proven knowledge of ML/AI platforms and workflows.

  • Ability to use and develop proven machine learning algorithms and related methods;

  • Ability to configure, use and develop data management systems;

  • Ability to understand the tools used by data scientists and experience in software development and test automation.

  • Software engineering skills;

  • Knowledge of distributed computing to ensure model learning;

  • Passion for leveraging data science to solve problems;

  • Ability to assess problems quickly, both qualitatively and quantitatively;

  • Team player with excellent organizational, interpersonal and communication skills;

  • Business/company values orientation;