Senior Machine Learning Engineer
Skills
Computer ScienceDevopsAdoptionApache SparkContinuous ImprovementDatabasesModeling
What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor’s degree in a quantitative field or equivalent experience
- 4+ years in Machine Learning Engineering or Data Engineering
- Hands-on expertise with Databricks technologies including Delta Lake MLflow Feature Store and Unity Catalog
- Experience delivering production-grade ML pipelines end to end
- Strong proficiency in Python PySpark and Spark SQL
- Deep understanding of distributed computing Spark optimization and scalable ML architecture
- Experience with AI-assisted development tools such as Cursor Claude or GitHub Copilot
- Experience with CI/CD and DevOps tooling such as GitHub Actions Azure DevOps or GitLab CI
- Hands-on experience with scikit-learn XGBoost or LightGBM
- Solid grasp of feature engineering experiment tracking model validation and performance evaluation
- Ability to mentor engineers and data scientists and lead technical discussions
- Experience building reusable ML frameworks and modernizing legacy workflows
Nice to have
- Cross-functional collaboration
- Governance mindset
- Mentoring ability
- Platform thinking
- Process improvement
Day to day
- Design and deploy scalable machine learning solutions using Databricks-native tools such as Delta Lake MLflow Feature Store AutoML and Model Serving.
- Build end-to-end ML pipelines that cover data ingestion feature engineering model validation deployment monitoring and retraining in a governed Lakehouse environment.
- Partner with data engineering analytics product and business teams to deliver reliable AI solutions improve platform standards and mentor junior team members.