Machine Learning Engineer z j. niemieckim

Convista Poland specializes in AI/ML solutions for clients in healthcare and the public sector, focusing on large-scale data environments and complex processes to improve automation and decision-making.

• Minimum 3 years of experience in machine learning in project or product environments.
• Experience deploying solutions to production environments, beyond model building or experimental analysis.
• Ability to work with clients and project teams to align technical solutions with real business needs.
• Experience working in enterprise environments.
• Comfortable working in teams and agile methodologies (Scrum/Kanban).
• Proficient in German and English at B2+ level for project work.
• Willingness to travel to client sites.
• Strong Python skills.
• Experience with ML libraries such as scikit-learn, PyTorch, or TensorFlow.
• Experience preparing processes for training, testing, and deploying models.
• Knowledge of MLOps tools and practices including automation, versioning, monitoring, and model maintenance.
• Experience with containerization and application/service deployment.
• Familiarity with good programming practices: clean code, testing, repository work, code reviews.
• Understanding of system architecture basics.
• Technical education background.

Nice to have:

• Experience in public sector projects, especially in health or social insurance.
• Knowledge of cloud solutions and tools such as MLflow, Airflow, Docker, Kubernetes, FastAPI, ollama.

• Develop machine learning solutions from concept to production deployment.
• Design, build, and enhance AI/ML models and integrate them with applications, services, and systems via APIs, including inference solutions used in business processes.
• Create and implement complete ML pipelines (end-to-end) covering data preparation, feature engineering, training, validation, deployment, and production availability.
• Develop MLOps practices including model lifecycle management, deployment automation, versioning, reproducibility, documentation, and standards for ML solutions.
• Work with solutions deployed in regulated production environments emphasizing security, data protection, auditability, and compliance.
• Collaborate closely with system architects, development teams, and business experts to ensure coherent integration of AI solutions within existing system and process landscapes.
• Cooperate with clients and project teams to identify AI applications, select appropriate approaches, and implement solutions delivering real business value.