MLOps Engineer

📋 Description

• Design and maintain end-to-end ML pipelines (data ingestion, training, deployment).
• Architect scalable model serving (API/Streaming) using Databricks, Seldon, SageMaker.
• Maintain centralized feature store for training and real-time inference.
• Develop CT pipelines to automate model retraining.
• Monitor ML assets for drift, skew, and latency.
• Collaborate with Data Scientists to productionize code.

🎯 Requirements

• Education: Bachelor’s or Master’s in CS/Engineering or related field.
• Experience: 6+ years in MLOps/DevOps/ML Engineering.
• ML Stack: Databricks/MLflow, Kubeflow, or SageMaker.
• Python with ML libraries (Scikit-Learn, PyTorch, TF) and SQL.
• Spark/PySpark for large-scale feature engineering.
• Airflow or ML orchestrators; Docker, Kubernetes, and ML CI/CD.

🎁 Benefits

• Great Company Culture: Creative and innovative environment.
• Growth: Global opportunities to learn and advance.
• Work Hard, Enjoy Life: Team events, gaming spaces, socials.
• Benefits: Medical, life insurance, holidays.
• Perks: Gym reimbursement, wellbeing programs, learning platforms.
• Employee discounts and free games.