Description

You own the design, maintenance, and optimization of end-to-end machine learning pipelines from data ingestion to production deployment.

This role is on-site.

Responsibilities

• Architect and manage scalable model serving infrastructure using tools such as Databricks Model Serving, Seldon, or SageMaker.
• Implement and maintain a centralized feature store to ensure consistency and low-latency access between training and real-time inference.
• Develop and optimize Continuous Training pipelines that automate model retraining based on performance decay or new data availability.
• Partner with Data Scientists to refactor experimental code into production-ready, modular, and testable components.
• Implement specialized monitoring for ML assets, tracking model drift, feature skew, and prediction latency to ensure high-quality player experiences.

Required Skills

• 6+ years of professional experience in MLOps, DevOps, or ML Engineering with a focus on productionizing ML at scale.
• Strong proficiency in Python and SQL.
• Hands-on experience with MLflow, Kubeflow, and AWS SageMaker.
• Practical knowledge of Scikit-Learn, PyTorch, and TensorFlow.
• Experience with big data processing using Spark and PySpark.
• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.

Preferred Skills

• Experience with real-time inference for high-concurrency applications and in-game personalization.
• Familiarity with Airflow, Docker, Kubernetes, and CI/CD principles.

Key Skills

Education

Any Graduate