ML Operations Engineer

March 24

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NextGen Healthcare

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NextGen Healthcare is a company that provides integrated health IT solutions to ambulatory practices. Their services include electronic health records (EHR), practice management, interoperability, patient engagement, and telehealth solutions. They aim to enhance the patient and provider experience through innovative technologies such as AI-driven workflows and mobile solutions. NextGen Healthcare also offers tailored solutions for various specialties and focuses on improving clinical and financial outcomes for their clients. They support practices of all sizes, from small offices to large enterprises, and emphasize the importance of interoperability and data exchange to enhance healthcare delivery.

EHR • financial • and HIE solutions for hospitals • health systems • physician practices

1001 - 5000 employees

Founded 1998

⚕️ Healthcare Insurance

☁️ SaaS

📡 Telecommunications

💰 Venture Round on 2015-02

📋 Description

• The Machine Learning Operations (MLOps) Engineer will support our AI/ML initiatives by streamlining the deployment, monitoring, and scaling of machine learning models in production environments. • 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. • Design and manage scalable infrastructure for training, testing, and serving machine learning models. • Automate data preprocessing, model training, and deployment workflows. • Monitor the performance of deployed models and systems, identifying and resolving issues proactively. • Optimize model inference latency, scalability, and resource utilization. • 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. • Maintain version control for datasets, models, and code. • Implement best practices for data and model governance, ensuring compliance with organizational and regulatory requirements. • Stay updated with the latest trends in MLOps tools, frameworks, and practices. • Recommend and implement improvements to the MLOps processes and infrastructure.

🎯 Requirements

• Education Required: Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. • Experience Required: 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 with 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).