Site Reliability Engineer - DevOps

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

We are looking for a motivated and detail-oriented Entry-Level MLOps Engineer to support our machine learning lifecycle and deployment infrastructure. You will work closely with data scientists, ML engineers, and DevOps teams to streamline model deployment, automate workflows, and ensure scalable and reliable ML systems.

Key Responsibilities:

• Assist in deploying, monitoring, and maintaining machine learning models in production.
• Help build and automate ML pipelines for data ingestion, training, validation, and deployment.
• Collaborate with cross-functional teams to integrate ML models with applications and services.
• Monitor model performance and implement alerts, logging, and automated retraining if needed.
• Support versioning and lifecycle management of datasets and models using tools like MLflow or DVC.
• Contribute to CI/CD pipelines tailored for ML systems.
• Document workflows, configurations, and deployment practices.
  
  
  

Required Qualifications:

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.

Understanding of machine learning concepts and model lifecycle.

Basic knowledge of Python and ML libraries (e.g., scikit-learn, TensorFlow, or PyTorch).

Familiarity with cloud platforms (AWS, GCP, or Azure) is a plus.

Exposure to containerization tools like Docker and orchestration tools like Kubernetes is a bonus.

Interest in DevOps principles and automation.

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