ML Operations Engineer

Job Description:

Model Deployment and Integration:

• Implement and maintain CI/CD pipelines for deploying machine learning models to production environments.

• Ensure seamless integration of machine learning models into existing software systems.

Infrastructure and Automation:

• Design and manage scalable infrastructure for training, testing, and serving machine learning models.

• Automate data preprocessing, model training, and deployment workflows.

 
Monitoring and Optimization:

• Monitor the performance of deployed models and systems, identifying and resolving issues proactively.

• Optimize model inference latency, scalability, and resource utilization.

 
Collaboration:

• Work closely with data scientists, software engineers, and product teams to understand requirements and deliver operational solutions.

• Collaborate with DevOps and cloud engineering teams to ensure infrastructure reliability and security.

Data and Model Management:

• Maintain version control for datasets, models, and code.

• Implement best practices for data and model governance, ensuring compliance with organizational and regulatory requirements.

Continuous Improvement:

• Stay updated with the latest trends in MLOps tools, frameworks, and practices.

• Recommend and implement improvements to the MLOps processes and infrastructure.

Perform other duties that support the overall objective of the position.

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

• Or, any combination of education and experience which would provide the required qualifications for the position.

• 2-3 years of hands-on experience in MLOps, DevOps, or related roles.
• Experience with MLOps tools and platforms like MLflow, Kubeflow, or SageMaker.
• Experience with feature stores and model versioning systems.
• Experience in building CI/CD pipelines using tools like Jenkins, GitLab CI, or similar.

• Knowledge of: Proficiency in Python and familiarity wStrong understanding of containerization and orchestration tools (e.g., Docker,
• Kubernetes). Strong understanding of containerization and orchestration tools (e.g., Docker, Kubernetes). Familiarity with distributed computing frameworks (e.g., Apache Spark). Knowledge of cloud platforms such as AWS, Azure, or Google Cloud. Solid understanding of model monitoring, logging, and debugging tools. Familiarity with database technologies and data pipelines (SQL, NoSQL, ETL/ELT processes).
• Skill in: Strong problem-solving skills and a detail-oriented mindset. Excellent communication and collaboration abilities.
• Ability to:    

NextGen Healthcare is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.