ML Engineer

As an ML Engineer at Rubiscape, you will build and operationalise machine learning models that power RubiStudio — our AutoML and MLOps studio trusted by Fortune 500 enterprises. You will work at the intersection of data engineering, feature design, and model training, turning raw enterprise data into production-grade predictive intelligence at scale. This role is central to Rubiscape’s mission of compressing the time from raw data to first production use case to under 90 days.

·         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.

·         Hands-on experience with AutoML frameworks (Auto-sklearn, FLAML, or similar) and their integration into governed ML platforms.

·         Exposure to regulated-sector modelling requirements such as model explainability under RBI or IRDAI guidelines.

·         Familiarity with Rubiscape or comparable unified analytics platforms (Databricks, Dataiku, or SageMaker Studio).

·         Published research or patents in applied machine learning.

Rubiscape is India’s leading Decision Intelligence Platform, unifying data engineering, BI, machine learning, and agentic AI in a single governed platform. Built in Pune and trusted by Fortune 500 enterprises across BFSI, manufacturing, healthcare, and government. 8 international innovation patents. 10 Industry-Academia Labs & COEs. From BI to AI — One Platform. Every Decision.

·         3+ years of hands-on ML engineering experience in a product or enterprise software environment.

·         Strong proficiency in Python with scikit-learn, XGBoost/LightGBM, and at least one deep learning framework (PyTorch preferred).

·         Practical experience with experiment tracking (MLflow or equivalent) and a structured approach to model versioning.

·         Solid understanding of feature engineering for tabular data, time-series, and event-based datasets common in enterprise analytics.

·         Experience deploying models as REST APIs or batch inference jobs in cloud or on-premises environments (AWS, Azure, or GCP).

·         Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline.