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Senior Machine Learning Engineer

4 weeks ago

india Prosperix Full time
Looking for a

Senior ML Ops Engineer

to build and manage machine learning infrastructure for dynamic pricing and personalized consumer experiences. The role focuses on developing robust ML workflows, including drift detection, model calibration, versioning, and reinforcement learning orchestration. The ideal candidate should have expertise in

Databricks, Unity Catalog, and feature stores , along with a strong understanding of

Git workflows, Databricks workflows, and automated ML training pipelines

Key Responsibilities:ML Infrastructure

– Build and maintain scalable ML infrastructure using Databricks, Unity Catalog, and feature stores.Drift Detection

– Develop frameworks to monitor data and model drift for reliable performance.Model Calibration & Versioning

– Ensure model accuracy and reproducibility through calibration and version control.Reinforcement Learning

– Optimize RL pipelines, including Contextual Bandits, for real-time, low-latency execution.Automated Training

– Create automated pipelines for model training, retraining, and validation.CI/CD for ML

– Implement CI/CD best practices for efficient model deployment and monitoring.Collaboration

– Work with ML Scientists to deploy and maintain models.Monitoring & Optimization

– Build tools for performance tracking and drift mitigation in production

Experience

–7+ years in MLOps or ML Engineering, managing ML workflows in production.ML Monitoring

– Hands-on experience with drift detection, model calibration, and performance tracking.Tech Stack

– Proficiency in

Databricks, Apache Spark, MLflow, Unity Catalog, and feature stores .Low-Latency ML

– Expertise in deploying real-time ML models, including reinforcement learning (Contextual Bandits, Q-learning).Automation

– Experience building

efficient, automated ML training pipelines .CI/CD & Version Control

– Strong knowledge of

Git workflows, CI/CD tools (e.g., GitLab) .Programming & Data Processing

– Proficiency in

Python, SQL, and Spark .ML Lifecycle Tools

– Familiarity with

MLflow, Kubeflow, Airflow

for model management