Senior Machine Learning Engineer - Databricks (m/f/*) chez Ultra Tendency

As a Senior Machine Learning Engineer, you will lead the design and implementation of advanced data and AI solutions on the Databricks Lakehouse Platform. Your focus will lie in building robust ML pipelines, enabling GenAI capabilities, and delivering production-grade solutions that support data-driven innovation at scale. 

WHAT WE OFFER

• A chance to accelerate your career and work with outstanding colleagues in a supportive learning community split across 3 continents
• Contribute your ideas to our unique projects and make an impact by turning them into reality
• Balance your work and personal life through our workflow organization and decide yourself if you work at home, in the office, or on a hybrid setup
• Annual performance review, and regular feedback cycles, generating distinct value by connecting colleagues through networks rather than hierarchies
• Individual development plan, professional development opportunities
• Educational resources such as paid certifications, unlimited access to Udemy Business, etc.
• Local, virtual, and global team events, in which UT colleagues become acquainted with one another

• Design, develop, and maintain scalable machine learning pipelines on the Databricks Lakehouse Platform using Spark, Delta Lake, and MLflow.
• Perform exploratory data analysis (EDA), data cleaning, feature engineering, and model evaluation to support data-driven use cases.
• Collaborate with data engineers and business stakeholders to translate real-world problems into ML solutions.
• Deploy models using MLflow, manage model versioning, and set up monitoring and retraining strategies.
• Optimize the performance and cost-efficiency of ML workloads on Databricks clusters.
• Apply best practices in MLOps, ensuring reproducibility, governance, and scalability of ML solutions.
• Guide and mentor team members on Databricks, ML engineering best practices 

WHAT YOU’LL BRING

• Hands-on experience with Databricks (on Azure, AWS, or GCP), especially Spark (PySpark or Scala), Delta Lake, and MLflow. 
• Deep understanding of the machine learning lifecycle, including data prep, modeling, evaluation, and deployment. 
• Proficiency in Python, with solid foundations in ML libraries (e.g., scikit-learn, pandas). 
• Experience building production-ready ML pipelines and working with large-scale datasets. 
• Familiarity with orchestration tools (e.g., Databricks Workflows, Airflow) and version control (Git/GitHub). 
• Strong problem-solving and communication skills, with the ability to explain complex models and logic to stakeholders. 
• Fluent English communication skills (written and spoken). 

Nice to have  

• Experience with MLOps workflows: model monitoring, automated retraining, A/B testing. 
• Exposure to cloud-native architectures, CI/CD for ML, and infrastructure-as-code (e.g., Terraform). 
• Knowledge of Databricks Unity Catalog for model governance and security. 
• Experience working in regulated or enterprise data environments.