Machine Learning Engineer

Company Description

Our Mission Statement

Digital and human resources at the center of the sustainable development of our society.

In a world of continuous transformation, accelerated by technological developments and societal challenges, it is necessary to adapt in an ongoing, agile way to meet the challenges of the future.

About Inetum

Inetum is a European leader in digital services. Inetum’s team of 28,000 consultants and specialists strive every day to make a digital impact for businesses, public sector entities and society. Inetum’s solutions aim at contributing to its clients’ performance and innovation as well as the common good.

Present in 19 countries with a dense network of sites, Inetum partners with major software publishers to meet the challenges of digital transformation with proximity and flexibility. Driven by its ambition for growth and scale, Inetum generated sales of 2.5 billion euros in 2023.

For more information, visit: www.inetum.com.

Job Description

Ensuring the building, training, and deployment of machine learning models using AWS SageMaker’s managed infrastructure and automation capabilities to develop scalable and efficient ML solutions.
Using Amazon Redshift and Amazon S3 for data storage, processing, and analysis required for ML model development and operations.
Applying Apache Spark and Apache Airflow for large‑scale data processing and pipeline orchestration, ensuring high performance and reliability.
Managing and optimizing machine learning workloads within Amazon EMR environments, while meeting performance and availability requirements.
Leveraging Python and key data science libraries (e.g., NumPy, Pandas, Scikit‑learn) for data manipulation, preprocessing, modeling, and analysis.
Collaborating with data engineering teams to ensure seamless and efficient integration of ML models into production environments.
Implementing and adhering to best practices for model versioning, monitoring, and CI/CD processes to maintain ML models in optimal condition throughout their lifecycle.

Qualifications

Minimum 3 years of hands‑on experience in designing, developing, and deploying machine learning models intended for production environments.
Strong and proven experience working with AWS services, including AWS SageMaker, Amazon Redshift, Amazon S3, Amazon EMR, as well as other AWS tools relevant to data processing and ML solution development.
Advanced proficiency in Python, along with core data science libraries such as NumPy, Pandas, and Scikit‑learn.
Demonstrated expertise using Apache Airflow and Apache Spark for pipeline orchestration and large‑scale data processing.

Benefits

Additional Information

Full access to foreign language learning platform
Personalized access to tech learning platforms
Tailored workshops and trainings to sustain your growth
Medical subscription
Meal tickets
Monthly budget to allocate on flexible benefit platform
Access to 7 Card services
Wellbeing activities and gatherings

Machine Learning Engineer

Key Skills

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