Global Healthcare IT, Inc. · 1 day ago

Machine Learning Operations Engineer

United States

Full-time

Remote

Senior Level

$70/hr - $75/hr

5+ years exp

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ConsultingElectronic Health Record (EHR)

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Responsibilities

Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability.

Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure.

Ability to lead engineering efforts in creating and implementing methods and workflows for ML/GenAI model engineering, LLM advancements, and optimizing deployment frameworks while aligning with business strategic directions.

Experience in developing AI pipelines for various data processing needs, including data ingestion, preprocessing, and search and retrieval, ensuring solutions meet all technical and business requirements.

Demonstrated ability to collaborate with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines for continuous improvement of machine learning models.

Expertise in implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes.

Competence in setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance.

Experience implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration.

Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations.

Skill in maintaining clear and comprehensive documentation of ML Ops processes and configurations.

Qualification

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Machine Learning EngineeringProduction DeploymentAI Pipeline DevelopmentCI/CD PipelinesAWSGCPAzureMonitoringLoggingVersion ControlSecurityComplianceContainerization TechnologiesHealthcare ExpertiseMaster’s DegreeMachine Learning CertificationDocumentation

Required

5 or more years relevant Machine Learning Engineer Experience

Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability.

Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Azure.

Expertise in implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes.

Competence in setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance.

Experience implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration.

Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations.

Skill in maintaining clear and comprehensive documentation of ML Ops processes and configurations.

Preferred

Experience with Docker, Kubernetes, or similar tools.

Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems, including integrating machine learning models with these systems.

Master’s Degree a plus

Bachelor’s Degree computer science, artificial intelligence, informatics or closely related field

Certification(s) in Machine Learning a plus