machine learning engineer for a recommendation system

The company’s client operates in the pharmaceutical industry and uses a globally deployed recommendation system.

• Develop and design the architecture of a globally used production recommendation system;
• Develop and optimize MLOps processes, including GitLab CI/CD pipelines and experiment management with MLflow;
• Deploy Machine Learning models to production using AWS SageMaker;
• Collaborate with Data Scientists to prepare scalable, production-ready ML solutions;
• Provide mentoring and technical support to Data Science, Data Engineering, and MLOps teams;
• Create high-quality Python code for data infrastructure and Machine Learning systems;
• Collaborate with Product Managers and business stakeholders to translate business needs into technical solutions;
• Identify and implement new technologies and best practices in Machine Learning and MLOps.

• At least 5 years of experience as a Machine Learning Engineer or in a related role;
• Master’s or doctoral degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field;
• Experience with model monitoring and system observability tools such as Prometheus, Grafana, or Evidently AI;
• Very strong Python programming skills and knowledge of its Data Science ecosystem;
• Experience designing, deploying, and maintaining production Machine Learning systems;
• Knowledge of AWS SageMaker and experience using it to train and deploy models;
• Practical MLOps experience, especially with MLflow and GitLab CI/CD;
• Ability to design scalable data architectures and ML pipelines;
• Knowledge of at least one Deep Learning framework: TensorFlow or PyTorch;
• Ability to translate business requirements into technical solutions;
• Ability to mentor and support the development of other team members;
• Fluent English at B2 level or higher.

• Long-term cooperation;
• Technical training, certifications, and professional development opportunities;
• Membership in the Mentoring Competence Center from the first day, with opportunities to attend conferences and share knowledge and experience;
• Clear career path;
• Employee benefits package including Multisport, private medical care, and life insurance;
• Friendly working atmosphere, integration events, and team-building meetings;
• Referral system with a bonus of up to 7000 zl.