Machine Learning Operations Engineer (16601)

Baer And Co - Baltimore, MD

Full-time - Mid Level

Hybrid - Baltimore, MD

Transportation Equipment Manufacturing

About the position

The Machine Learning Operations Engineer will be responsible for designing, developing, and managing production-level machine learning pipelines for a 9-month federal project in Baltimore, MD. This role focuses on automating the deployment of machine learning models into production environments, ensuring smooth integration with existing systems, and implementing monitoring tools to track model performance. The engineer will collaborate closely with data scientists and software engineering teams to optimize models and data pipelines, ensuring they meet performance, scalability, and security requirements.

Responsibilities

• Design, develop, and manage production-level ML pipelines for model training, validation, and deployment.
• Automate the deployment of machine learning models into production environments using MLOps tools (e.g., Kubeflow, MLflow, TFX).
• Ensure smooth integration of models into existing production systems and services.
• Implement model versioning and rollback strategies for seamless deployments and updates.
• Develop and implement monitoring tools and frameworks to track model performance in production environments.
• Monitor for model drift, performance degradation, and anomalies and initiate corrective actions.
• Ensure continuous optimization and fine-tuning of models based on real-time data and feedback.
• Work closely with data scientists to understand and implement the requirements for model deployment.
• Collaborate with software engineering teams to ensure models integrate smoothly into production applications and services.
• Ensure that models meet performance, scalability, and security requirements.
• Build and optimize end-to-end data pipelines for data preprocessing, feature engineering, and model inference.
• Ensure data quality and consistency through robust data validation and transformation pipelines.
• Work with the team to handle large-scale data processing for real-time and batch predictions.
• Write clear, concise, and maintainable documentation for MLOps processes, pipelines, and workflows.
• Define and enforce best practices for model development, deployment, and monitoring.
• Contribute to the development of internal tools and frameworks to streamline machine learning operations.
• Stay up-to-date with the latest developments in MLOps, machine learning frameworks, and deployment technologies.
• Continuously evaluate and integrate new tools, technologies, and methodologies to improve the scalability, efficiency, and reliability of ML systems.

Requirements

• 5+ years of professional experience working with machine learning models, data pipelines, and production environments.
• Proven experience in deploying and maintaining machine learning models in production systems.
• Hands-on experience with MLOps tools and frameworks such as Kubeflow, MLflow, TensorFlow Extended (TFX), and/or similar technologies.
• Strong background in statistical methods, data preprocessing, and feature engineering for machine learning.
• Master's degree in Computer Science, Data Science, Engineering, or a related field with 5+ years of experience in machine learning or MLOps, OR Bachelor's degree in Computer Science, Data Science, Engineering, or a related field with 7+ years of experience in machine learning or MLOps.
• Public Trust Security Clearance.