ML Engineer

Rubiscape; Pune District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Rubiscape, based in Pune, seeks an ML Engineer to build and operationalise machine learning models powering RubiStudio, our AutoML and MLOps studio used by Fortune 500 enterprises. You will transform raw enterprise data into production-grade predictive intelligence at scale, helping shorten time to first production use cases to under 90 days.

This role requires hands-on Python, scikit-learn, XGBoost/LightGBM, and PyTorch, plus experience with MLflow and feature engineering for tabular data

Qualifications

• 3+ years hands-on ML engineering 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.

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 Rubiscapes 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 RubiStudios 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 Rubiscapes 10 Industry-Academia COEs to prototype novel modelling approaches.

Skills

Python programming

Experiment tracking

Model deployment

Feature engineering

Education

Bachelor's or Master's degree in Computer Science/Statistics/Mathematics

Tools

Python

scikit-learn

XGBoost/LightGBM

PyTorch

MLflow

RubiFlow

RubiStudio

Job description

About the Role

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 Rubiscapes mission of compressing the time from raw data to first production use case to under 90 days.

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 Rubiscapes 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 RubiStudios 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 Rubiscapes 10 Industry-Academia COEs to prototype novel modelling approaches.

Nice to Have

• 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.

About Rubiscape

Rubiscape is Indias 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.

Requirements

• 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).
• Bachelors or Masters degree in Computer Science, Statistics, Mathematics, or a related quantitative discipline.