MLOps Engineer

Full-Time

Remote

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Overview:

Join Azra AI on its mission to improve healthcare through innovative applications of natural language processing (NLP). At Azra AI, we enable health systems to enhance clinical workflows by analyzing pathology and radiology reports in real-time, identifying the presence and type of cancer, and automating registry abstraction through text extraction. These reports are presented to clinicians in an intuitive workflow tool, allowing them to provide timely care to patients while focusing on what they do best—saving lives.

Your Adventure at Azra AI:

The MLOps Engineer will be responsible for building and maintaining scalable machine learning pipelines, automating the deployment and monitoring of models, and integrating continuous integration/continuous deployment (CI/CD) practices into our AI workflow. This role focuses on operationalizing machine learning models, ensuring they are robust, scalable, and easy to manage in production.

Key Responsibilities:

• Design and implement ML pipelines to deploy machine learning models.
• Automate the deployment, monitoring, and retraining of models.
• Work with data scientists and engineers to move models from development to production.
• Ensure scalability, security, and robustness of deployed models in cloud environments (GCP).
• Implement CI/CD pipelines for continuous model delivery and improvement.
• Monitor model performance and implement monitoring/alerting systems for production models.
• Collaborate with data engineers to manage data pipelines and integration for model training and deployment.
• Optimize cloud infrastructure (GCP) for cost-effectiveness and performance.

Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Experience with MLOps tools and frameworks (e.g., Kubeflow, MLflow, TensorFlow Extended).
• Strong programming skills in Python, with experience in Docker, Kubernetes, and cloud infrastructure (GCP preferred).
• Familiarity with CI/CD tools like GitLab, Jenkins, or equivalent.
• Experience in deploying, monitoring, and scaling machine learning models in production environments.
• Knowledge of data pipelines and data engineering workflows.