Lead AIML Engineer R
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Job Location:
Guadalajara - Mexico
Monthly Salary:
Not Disclosed
Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
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.
We may use artificial intelligence (AI) tools to support parts of the hiring process such as reviewing applications analyzing resumes or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed please contact us.
Key Skills
- Administrative Skills
- Facilities Management
- Biotechnology
- Creative Production
- Design And Estimation
- Architecture
About Company
Brillio is a global leader in Enterprise Digital Transformation Solutions, providing strategic consulting services and solutions using emerging technologies.
