Machine Learning Engineer

4w1 month ago

Keystone Solutions

Brussels, BE · Contract · €75,000 – €105,000

About this role

As a Keystone Solutions consultant, you will be deployed on a consultancy mission at the client, delivering robust, scalable, and maintainable machine learning solutions integrated into existing systems and data flows in Azure cloud and on-premise environments. The client features a dynamic organization with an informal culture and bilingual (Dutch and French) setting. You will ensure models are performant, reliable, and manageable in production with focus on reproducibility, monitoring, and regulatory compliance including the European AI Act.

Under the consultancy model, work closely with client stakeholders on site and remotely, embedding within multidisciplinary teams using Agile SAFe to translate ML use cases into sustainable implementations. Align with client processes while adhering to Keystone's engineering standards. Engage with diverse challenges from data preparation and feature engineering to deployment and monitoring of ML services.

Operate in IT teams that are multidisciplinary within an Agile SAFe framework. Exposure to variety of use cases such as classification, regression, forecasting, detection, and scoring across Azure and on-premise contexts. Contribute to best practices in ML engineering, testing, deployment, and monitoring.

Benefit from hands-on consultancy work and pairing with data engineers, developers, and architects for turbo-charged learning in MLOps, CI/CD, and reliable ML delivery. Foster career growth by taking ownership of technical implementations and proposing improvements. Strengthen communication with technical and non-technical stakeholders across client organizations.

Embody Keystone Solutions values of excellence, collaboration, clarity, and continuous learning on every mission. Uphold quality, reliability, and maintainability standards. Bring these values to client projects through proactive contributions.

Requirements

Experience delivering ML solutions integrated into existing systems and data flows in Azure cloud and on-premise environments
Proficiency in designing scalable and maintainable ML pipelines for production reliability
Strong focus on reproducibility, monitoring, and regulatory compliance including the European AI Act
Skills in data preparation, feature engineering, and ensuring data quality and consistency
Ability to develop models for classification, regression, forecasting, detection, and scoring
Familiarity with Agile SAFe in multidisciplinary teams
Knowledge of MLOps, CI/CD, and ML deployment practices
Experience embedding in client teams for sustainable technical implementations

Responsibilities

Design, build, deploy, and maintain machine learning models and ML pipelines ensuring reliability, scalability, and manageability in production across Azure and on-premise
Work closely with data engineers, developers, architects, and business stakeholders to translate ML use cases into sustainable technical implementations compliant with regulations like the European AI Act
Process, analyze, and prepare data from various internal and external sources
Design and implement data transformations and feature engineering processes
Safeguard data quality, consistency, and reproducibility within ML workflows
Collaborate with teams to make data reliably and reusably available for ML use cases
Design, train, test, and tune machine learning models for use cases such as classification, regression, forecasting, detection, or scoring
Implement monitoring, continuous improvement, and reproducibility for production ML models

Benefits

Hands-on consultancy work across diverse client projects and ML use cases
Pairing with data engineers, developers, and architects for broad learning
Growth in MLOps, CI/CD, monitoring, and reliable ML delivery through real-world engagements
Ownership of technical implementations and proactive improvements
Exposure to dynamic, informal, bilingual environment with Agile SAFe
Strengthened communication with technical and non-technical stakeholders