Machine Learning Platform Engineer

Location: Pune
Experience: 9–13 Years
Work Mode: Work from Office
Notice Period: Immediate to 45 Days(889)
Qualification: Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.

Role Overview

We are looking for an experienced MLOps Engineer to manage and maintain traditional Machine Learning models in production environments.

The role focuses on deploying predictive models, managing retraining cycles, monitoring model performance, identifying data and model drift, and ensuring reliable model operations. You will work closely with Data Science, Data Engineering, and business teams to maintain the accuracy, stability, and performance of machine learning solutions.

This is a hands-on operational role involving production model management, deployment pipelines, performance tracking, and model lifecycle maintenance. The primary focus is on conventional predictive ML models rather than Generative AI or LLM-based applications.

Key Responsibilities

1. Model Deployment & Lifecycle Management

• Manage the deployment of trained machine learning models into production environments.

• Execute model packaging, validation, testing, and release activities through established CI/CD pipelines.

• Coordinate model retraining and deployment cycles based on business and operational requirements.

• Maintain model versions, deployment records, and associated lifecycle documentation.

• Ensure new model releases are validated before being moved into production.

2. Model Monitoring & Drift Detection

• Monitor production models to ensure consistent accuracy and performance.

• Identify data drift, feature drift, and concept drift that may impact model predictions.

• Generate regular monitoring reports and highlight deviations from defined performance thresholds.

• Investigate changes in model behaviour and coordinate corrective actions with relevant teams.

• Track model performance over time and identify opportunities for improvement.

3. Model Validation & Performance Optimization

• Evaluate updated models against existing production models before deployment.

• Perform backtesting and champion-challenger comparisons to assess model effectiveness.

• Validate prediction quality and ensure that model changes meet expected performance standards.

• Execute optimization routines to identify suitable interventions based on model predictions.

• Support controlled model promotion and rollback activities as per approved procedures.

4. Model Governance & Reporting

• Maintain model lineage, including version history, training data, baselines, and previous stable releases.

• Prepare reports covering model performance, operational costs, and resource utilization.

• Ensure model documentation and tracking records remain accurate and up to date.

• Maintain visibility into model deployment, retraining, and monitoring activities.

5. Data Quality & Cross-functional Coordination

• Identify data quality issues that may affect model accuracy and production performance.

• Work with Data Engineering and Data Science teams to investigate and resolve data-related concerns.

• Communicate technical issues, operational updates, and performance findings to relevant stakeholders.

• Ensure timely escalation of incidents and deviations that require further investigation.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related discipline.

• Strong 4+yrs of experience in MLOps, ML Engineering, Data Engineering, or DevOps.

• Hands-on experience deploying and maintaining machine learning models in production.

• Practical experience with customer churn prediction models.

• Strong programming skills in Python.

• Experience working with CI/CD pipelines for machine learning workflows.

• Understanding of model registries, version control, and model lineage.

• Knowledge of model retraining, validation, and production release processes.

• Experience identifying and monitoring data drift, feature drift, and model performance changes.

• Understanding of data processing, model evaluation, and performance reporting.

• Ability to work with cross-functional teams and communicate technical updates effectively.

Good to Have

• Experience in telecommunications or large-scale data environments.

• Exposure to predictive use cases such as customer churn, fraud detection, and forecasting.

• Knowledge of champion-challenger testing and backtesting techniques.

• Experience with cloud-based machine learning platforms and services.

• ML or cloud-related certifications.

• Experience working with distributed data and engineering teams.

Work Requirements

• Location: Pune.

• Work Mode: Work from Office.

• Shift: Rotational shifts, including occasional weekend and public holiday coverage.

• Candidates should be comfortable working in a shift-based operational environment.

• Strong verbal and written communication skills are essential for regular coordination with distributed teams.

- **Shift:** Rotational shifts, including occasional weekend and public holiday coverage.
- Candidates should be comfortable working in a shift-based operational environment.
- Strong verbal and written communication skills are essential for regular coordination with distributed teams.