Senior Engineer, Machine Learning Operations

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!

Are you passionate about building and deploying machine learning models in production? Do you thrive in a collaborative environment and enjoy tackling complex technical challenges? If so, then we want to hear from you!

We are seeking a talented and experienced Machine Learning Operations (MLOps) Engineer to join our team. As an MLOps Engineer, you will play a critical role in deploying, maintaining, and optimizing machine learning models. Also, you will be responsible for the entire lifecycle of our models, from development to production. You will work closely with data scientists, software engineers, and other stakeholders to ensure the seamless integration of machine learning solutions into our products and services.

Responsibilities:

• Design and implement CI/CD pipelines for machine learning models using tools like GitLab CI, GitHub Actions, CircleCI, or Airflow.
• Deploy machine learning models into production environments, establish monitoring systems to track model performance and data drift, and implement automated alerts for potential issues.
• Manage and optimize the infrastructure required for running machine learning models, including cloud resources, containers, and orchestration tools. Containerize, version control, and deploy machine learning models.
• Implement version control systems for machine learning models and collaborate with data scientists and engineers to streamline the model development and deployment process.
• Develop automation scripts and tools to streamline the deployment and scaling of machine learning solutions, ensuring high availability and reliability.
• Implement security best practices for machine learning systems, ensure compliance with data privacy regulations, and participate in security audits and assessments.
• Identify opportunities to optimize the performance of machine learning models, including tuning hyperparameters, improving inference speed, and reducing resource consumption.
• Stay up to date with the latest advancements in MLOps and related technologies.
• Document processes and best practices for MLOps within the team.

Qualifications:

• Bachelor’s or master’s degree in computer science, Engineering, or a related field (or equivalent experience).
• Proven experience in machine learning operations, DevOps, or software engineering roles.
• Strong understanding of CI/CD principles and experience with CI/CD tools.
• Strong programming skills in languages such as Python, Java, or Go.
• Proficiency in containerization technologies (Docker, Kubernetes) and orchestration tools.
• Experience with MLOps tools (MLflow, Kubeflow, Metaflow) is a plus.
• Familiarity with machine learning frameworks (TensorFlow, PyTorch, scikit-learn) is a plus.
• Excellent communication and collaboration skills.
• Ability to work independently and as part of a cross-functional team.
• Problem-solving skills and a passion for learning new technologies.