MLOps Engineer

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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.

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.

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