📋 Description

• Senior MLOps engineer to build and scale ML infrastructure for production models.
• Operationalize ML workflows across training, validation, deployment, monitoring, and retraining.
• Collaborate with ML Scientists and engineers to productionize experimentation pipelines.

🎯 Requirements

• 7+ years in MLOps/ML Engineering or closely related roles with production ML experience.
• Proven drift detection, model calibration, monitoring, automated retraining, and ML infrastructure.
• Hands-on Databricks suite experience: Databricks, Apache Spark, Unity Catalog, MLflow, feature
• Experience with low-latency ML orchestration, including reinforcement learning approaches
• Automation of ML training, validation, retraining, and deployment pipelines; strong CI/CD practices.
• Programming in Python and SQL; processing large-scale data with Spark or similar.

🎁 Benefits

• Opportunity to work on advanced ML infrastructure for dynamic pricing and personalization.
• Exposure to Databricks, Spark, MLflow, Unity Catalog, feature stores, and RL workflows.
• Ownership across the full ML lifecycle from training to monitoring.
• Collaborative environment with ML Scientists and engineering teams.
• Opportunities to build scalable automation and infrastructure improving reliability.
• Competitive compensation and professional development opportunities.