Machine Learning Engineer at Inetum

Bucharest, , Romania -

Full Time

Start Date

Immediate

Expiry Date

21 Apr, 26

Salary

0.0

Posted On

21 Jan, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, AWS SageMaker, Amazon Redshift, Amazon S3, Apache Spark, Apache Airflow, Python, NumPy, Pandas, Scikit-learn, Data Engineering, Model Versioning, CI/CD, Data Processing, Pipeline Orchestration, Model Deployment

Industry

IT Services and IT Consulting

Description

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. Additional Information Benefits: 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

Responsibilities

The role involves building, training, and deploying machine learning models using AWS SageMaker and managing data processing with Amazon Redshift and S3. Collaboration with data engineering teams is essential for integrating ML models into production environments.