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.

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;