Senior Machine Learning Engineer

We are looking for



 

We are looking for a driven and enthusiastic Senior Machine Learning Engineer (m/w/d) , who desires to contribute to the growth and success of our Data & Analytics Team.

• As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying ML-powered data products that enhance our value proposition and drive business impact.
• You will collaborate closely with Data Analysts to translate business needs and KPIs into modeling approaches, and with Data Engineers to ensure robust data pipelines, feature availability, and production-ready systems.
• You will own the full lifecycle of ML models, from problem framing and experimentation to deployment, monitoring, and continuous improvement, ensuring that models deliver measurable value over time.
• You will contribute to defining best practices for ML development, including model evaluation, versioning, and performance monitoring, while promoting a pragmatic and impact-driven approach to AI/ML within the company.

• 5-8 years of experience in Machine Learning, Applied Data Science, or similar roles
• Strong proficiency in Python and common ML libraries (e.g. scikit-learn, TensorFlow, PyTorch)
• Experience developing and deploying ML models in production environments
• Solid understanding of core ML techniques (supervised and unsupervised learning)
• Experience with feature engineering and working with real-world datasets
• Familiarity with APIs and model serving (batch and/or real-time inference)
• Familiarity with Large Language Models and their use within end-to-end AI workflows
• Strong problem-solving skills and ability to translate business problems into ML solutions
• Ability to work independently and take ownership of projects end-to-end
• Master’s degree in Computer Science, Engineering, Mathematics, or a related field. A PhD in a relevant discipline is a plus.
• English proficiency (written and spoken, minimum B2 level)

• Experience with experimentation frameworks (A/B testing) and impact measurement
• Experience with cloud platforms (AWS preferred) and containerization (Docker, Kubernetes)
• Knowledge of MLOps practices (model deployment, versioning, monitoring, retraining)