Get to know the role
As a Machine Learning Engineer, you will build scalable and production-grade ML systems across domains like payments, lending, and insurance. You'll develop predictive models using a blend of machine learning and deep learning techniques. You will engineer high-quality features from diverse data sources, validating model performance on real-world datasets, and implementing pipelines using tools like Python, Spark, and SQL. You'll collaborate with data scientists, product managers, and engineers to translate our needs into ML solutions. A foundation in ML concepts, hands-on coding ability, and efficiently are key to succeeding in this role.
The Critical Tasks You Will Perform
- Build and deploy scalable ML models using Python, Spark, and cloud-native tools.
- Develop data pipelines and feature stores to support model training and inference.
- Validate models on new datasets, based on in-market performance.
- Engineer predictive features from internal data assets to build refined customer profiles. Identify external data assets to bring into the model mix.
- Identify model gaps or performance drifts and lead model refresh cycles.
- Present findings to senior leadership with clear articulation of risk trade-offs and growth.
- Translate model insights into strategic recommendations (e.g., policy changes, pricing levers, customer targeting strategies).
- Work with cross-functional teams to gather requirements and align ML solutions with product goals.
- Debug and improve models based on performance drifts or unexpected outcomes.