Machine Learning Operations Engineer at Baubap

The Machine Learning Operations Engineer's mission:

The mission of the Machine Learning Operations Engineer at Baubap is to design and implement robust CI/CD pipelines, ensuring seamless deployment and versioning of machine learning models, indicators, and data processes. This role is critical in enhancing the scalability and reliability of our ML systems while upholding Baubap’s high standards of data integrity, compliance, and performance.

You will work closely with data scientists, engineers, and stakeholders to automate workflows, improve collaboration, and ensure that Baubap's machine learning operations are state-of-the-art.

The expected outcomes:

• Fully operational CI/CD pipelines that automate the deployment of ML models and indicators.
• Clear versioning and tracking of all data, models, and processes.
• Robust monitoring systems for real-time insights into model performance and data integrity.
• Scalable infrastructure for efficient training and deployment of models across diverse use cases.
• Comprehensive documentation and training materials to support collaboration and scalability.

The day to day tasks:

• Design, implement, and manage CI/CD pipelines for ML model deployment and monitoring.
• Automate model training, validation, and deployment processes for seamless integration into production systems.
• Maintain versioning for data, models, and training processes, ensuring reproducibility and traceability.
• Develop and manage infrastructure for scalable model training and deployment using cloud platforms (e.g., AWS, GCP, Azure).
• Act as an architect by designing foundational building blocks and frameworks that support Baubap's projects, ensuring they are scalable, robust, and aligned with business objectives.
• Optimize resource utilization for training and inference workloads, including distributed computing setups (e.g., Kubernetes, Coiled).
• Implement robust monitoring systems for deployed models, tracking metrics like accuracy, drift, and performance.
• Ensure model reliability by building automated alerting systems for anomalies in predictions or data.
• Define rollback strategies for underperforming models and create mechanisms to retrain or replace them.
• Work closely with data science, engineering, and risk teams to align ML pipelines with business objectives.
• Create detailed documentation for all processes, workflows, and infrastructure components.
• Contribute to cross-functional knowledge sharing by creating training materials and conducting workshops.
• Implement data validation and preprocessing pipelines for high-quality training datasets.
• Maintain and manage feature stores and model registries.
• Ensure compliance with industry regulations and data privacy standards.
• Stay up-to-date with the latest trends in MLOps and propose improvements to existing systems.
• Explore and integrate emerging tools and frameworks to enhance productivity and reliability.
• Evaluate and implement best practices for managing model complexity and the bias-variance tradeoff.

Why You should apply:

• You have strong programming skills in Python and experience with MLOps frameworks (e.g., MLflow, Kubeflow, TensorFlow Extended).
• You've worked and understand cloud platforms like AWS, GCP, or Azure.
• You have experience with containerization and orchestration tools (Docker, Kubernetes).
• You have close familiarity with CI/CD tools (e.g., Jenkins, GitHub Actions, GitLab CI/CD).
• You are an expert in data preprocessing, validation, and feature engineering pipelines.
• You have deep knowledge in model versioning and reproducibility tools.
• You've worked with monitoring frameworks for ML (e.g., Evidently, Prometheus, Grafana).
• You have strong understanding of A/B testing, model drift, and performance tracking.

What we can offer:

• Being part of a multinational, highly driven team of professionals.
• Flexible and remote working environment.
• High level of ownership and independence.
• 20 vacation days / year + holiday bonus.
• 1 month (proportional) of Christmas bonus.
• Competitive salary payed in USD.

*Although the position is fully remote, we are only accepting LATAM based candidates.