7+ years of experience in MLOps or ML Engineering, deploying and managing production ML workflows
Proficient with Databricks, Apache Spark, MLflow, Unity Catalog, and feature stores
Experience deploying low-latency ML models and orchestrating reinforcement learning solutions (e.g., Contextual Bandits, Q-learning)
Strong knowledge of Git workflows, CI/CD practices, and Python, SQL, and Spark
Requirements:
ML Infrastructure Development on Databricks, leveraging Unity Catalog and feature stores to build scalable ML infrastructure
Drift Detection and Monitoring Frameworks to ensure continuous monitoring and reliability of ML models in production
Automated Training Pipelines with model calibration and versioning to enable efficient experimentation and deployment
CI/CD for ML and Collaboration with ML Scientists to ship, deploy, and maintain models
Job description
Job Summary: We seek a Senior ML Ops Engineer to play a critical role in operationalizing machine learning workflows that drive dynamic pricing and personalized consumer experiences. This position focuses on building robust ML infrastructure and frameworks, including drift detection, model calibration, versioning, and reinforcement learning orchestration. The ideal candidate will bring expertise in Databricks, Unity Catalog, and feature stores and a deep understanding of Git workflows, Databricks workflows, and automated ML training pipelines. Qualifications:
7+ years in MLOps, ML Engineering, or related roles, focusing on deploying and managing ML workflows in production environments. Hands-on experience building drift detection systems, model calibration frameworks, and robust monitoring tools for ML pipelines.
Proficient in using Databricks, Apace Spark, ML Flow, Unity Catalog, and feature stores.
Expertise in deploying and orchestrating low-latency ML models, including reinforcement learning solutions like Contextual Bandits and Q-learning.
Experience designing automated training pipelines for ML models, focusing on efficiency
Strong knowledge of Git workflows, CI/CD practices, and tools like GitLab or similar.
Proficiency in Python, SQL, and big data processing tools like Spark.
Familiarity with ML lifecycle tools such as MLflow, Kubeflow, and Airflow.
Strong understanding of model performance monitoring, drift detection, and retraining workflows.
Key Responsibilities
ML Infrastructure Development: Build and maintain scalable ML infrastructure on Databricks, leveraging Unity Catalog and feature stores to support model development and deployment.
Drift Detection Frameworks: Design and implement frameworks for detecting data and model drift, ensuring continuous monitoring and high reliability of ML models in production.
Model Calibration & Versioning: Develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle.
Low-Latency Orchestration: Design and optimize reinforcement learning (RL) orchestration pipelines, including Contextual Bandits, for real-time execution in low-latency environments.
Automated Training Pipelines: Create automated frameworks for training, retraining, and validating ML models, enabling efficient experimentation and deployment.
CI/CD for ML: Implement CI/CD best practices to streamline the deployment and monitoring of ML models, integrating with Databricks workflows and Git-based version control systems.
Collaboration: Work closely with ML Scientists to ship, deploy, and maintain models.
Monitoring & Optimization: Build tools for model performance monitoring, operational analytics, and drift mitigation, ensuring reliable operation in production environments.
