Lead AI/ML Engineer - R01557649

Primary Skills

• Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

Specialization

• Data Science Advanced: AI/ML Engineer

Job requirements

• The Senior MLOps Engineer will play a critical role in operationalizing machine learning workflows that drive dynamic pricing and personalized consumer experiences.
• This position is foundational for sustaining and scaling the ML ecosystem, enabling faster deployments, reliable model governance, and automation across the Consumer ML & Personalization team. The role ensures continuity, protects experimentation velocity, and preserves operational maturity for all production ML systems and go-to-market testing.
• • Build and maintain scalable ML infrastructure on Databricks, leveraging Unity Catalog and feature stores to support model development and deployment.
• • Establish automated, production-ready ML pipelines for multi-model inference, self-serve test orchestration, contextual bandits, and advanced reporting. Drift Detection & Model Observability
• • Design and implement frameworks for detecting data and model drift, ensuring continuous monitoring and high reliability of ML models in production.
• • Standardize retraining, monitor inference drift, and automate performance checks to prevent stale models and undetected errors. Model Calibration & Versioning
• • Develop model calibration frameworks and establish versioning practices to maintain transparency and reproducibility across the ML lifecycle.
• • Maintain CI/CD checks, versioning, and consistent environments between Dev and Prod. 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


• 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 Databricks, Apache Spark, MLflow, 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.