ML Engineer

About the Role

Key Responsibilities

• · Design, train, and evaluate supervised and unsupervised ML models across BFSI, manufacturing, and healthcare verticals using Python, scikit-learn, and PyTorch.
• · Build and maintain feature engineering pipelines integrated with Rubiscape’s internal Feature Store, ensuring consistency between training and inference environments.
• · Collaborate with data engineers on RubiFlow to define feature contracts and manage data quality upstream of model training.
• · Integrate trained models into RubiStudio’s model registry and automate versioning, lineage capture, and metadata tagging via MLflow.
• · Profile model performance across data slices; diagnose drift, bias, and degradation using monitoring hooks connected to RubiSight dashboards.
• · Write clean, type-annotated Python code that meets production standards and can be reviewed, tested, and deployed by the platform engineering team.
• · Participate in quarterly Innovation Lab collaborations with Rubiscape’s 10 Industry-Academia COEs to prototype novel modelling approaches.