Lead Ai/ml Engineer

hace 2 semanas

Guadalajara, México Brillio A tiempo completo

**Lead AI/ML Engineer - R **:**Lead AI/ML Engineer****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.

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