Company Description

Why INETUM?

We are an international, agile digital consultancy. In the post-digital transformation era, we strive to ensure that each of our 28,000 professionals can continuously renew themselves.

Each of them can design their professional journey according to their preferences, collaborate with their clients to practically build a more positive world, innovate in each of the 27 countries, and balance their professional career with their personal well-being.

Our 28,000 digital athletes are proud to have been certified as a Top Employer Europe 2024.

Job Description

The DS&IA engineer identifies the needs of our customers and INETUM's internal Infrastructure teams. His or her skills and methods enable him or her to draw up specifications and identify the resources needed to ensure the success of the projects entrusted to him or her. He/she participates in data structuring, the development of artificial intelligence algorithms, the industrialization of artificial intelligence models in applications, technology watch on data science tools, etc.

His skills in statistics will enable him to support the construction of machine learning models, and his knowledge of IT will help him anticipate their production launch.

He/she will be the main contact for INETUM's Innovation teams.

He/she will report to the GBLO QASI Industrialization & Tooling organization.

Qualifications

Profile: minimum 2 years' experience

- Experience in the use of methods and techniques related to MLOps (Data versioning, Experimentation, Model registries and model supervision).

Technical skills :

- Expertise in machine learning algorithms and methods,

- Mastery of various neural network architectures and associated development environments/libraries (TensorFlow, PyTorch, Keras, scikit-learn...)

- Proficiency in various operating systems (Linux, Windows...)

- Expertise in databases and database management (SQL/NoSQL) and vector databases

- Proficiency in programming languages (C++, Java, Python, R...) and microservices architectures

- Mastery of application packaging: Docker, PodMan, Kubernets

- Mastery of DevOps and SRE mode development through CI/CD-type development chains

- Mastery of prediction techniques: Regressions, Arima, Prophet and classification techniques: Random Forest, XGboost, LightGBM

- Hyperscalers: Azure, GCP, AWS,

- ELK (AI module), opensource world

- Knowledge of how to use LLMs

- Would be a plus: knowledge of libraries: datarobot, H2O Prediction, Dataku, ...