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.
