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AI Operations (MLOps)
JOB SUMMARY
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Engineering - AI Operations (MLOps) Summary:OpenNetworks is a healthcare technology platform transforming American healthcare by creating an open marketplace that connects providers and purchasers.
We're removing barriers in the healthcare system to enable transparent, efficient, and cost-effective care delivery.
Our platform leverages cutting-edge technology, including AI, to address systemic challenges in healthcare access, pricing, and quality. Role OverviewWe are seeking a highly skilled and experienced AI Operations Engineer to join our engineering team.
This critical role is responsible evangelism for internal AI adoption as a force multiplier, as well as for building, deploying, and managing the end-to-end lifecycle of our machine learning models, which are central to optimizing our open marketplace and driving healthcare insights.
The ideal candidate will have an engineering background and bridge the gap between Data Science and Core Engineering, ensuring that our AI models are reliable, scalable, secure, and compliant in a high-stakes production environment.
Internal AI Force Multiplier: Educate and evangelize AI to boost productivity across all internal groups. Model Serving Deployment: Build scalable, high-availability models from claims, contracts, employer, employer and price transparency data to create intelligence and handle real-time inference requests for the OpenNetworks platform.
Infrastructure Management: Manage and optimize the underlying cloud infrastructure (compute, storage, and networking) for both large-scale model training and high-throughput serving environments. POC/Prototyping: Proficient in using rapid coding techniques to create POCs to demonstrate new AI capabilitiesModel Monitoring ReliabilityPerformance Monitoring: Implement advanced monitoring and alerting systems to track model performance, data drift, and prediction latency in production. Model Governance: Establish processes for model versioning, lineage tracking, and rollback capabilities to ensure operational integrity and auditability. Security Compliance: Ensure all MLOps practices and data handling adhere strictly to healthcare regulatory standards, including HIPAA and data privacy best practices. Collaboration OptimizationData Scientist Partnership: Collaborate directly with the Data Science team to containerize model code, optimize models for production serving efficiency, and transition models from research to production-readiness. Automation: Automate all aspects of the model lifecycle, from data ingestion and feature engineering to deployment and retraining triggers. Resource Efficiency: Continuously evaluate and implement technologies to improve the speed, cost-efficiency, and reliability of the overall ML system. Required
Qualifications
Experience: 3+ years of experience in MLOps, DevOps, or Software Engineering with a focus on machine learning systems. Programming: Expert proficiency in Python and solid experience with writing clean, production-level code. Cloud Containerization: Strong experience with a major cloud provider (AWS, GCP, or Azure) and expert knowledge of containerization technologies (Docker, Kubernetes). MLOps Tools: Hands-on experience with MLOps frameworks and platforms (e. g. , MLflow, Kubeflow, Sagemaker, TFX, or similar). Technical Foundation: Deep understanding of the machine learning lifecycle, from data prep and model training to deployment and monitoring. Preferred
Qualifications
Experience working in the HealthTech or FinTech industries, particularly with highly regulated data. Experience designing and managing data pipelines (ETL/ELT) for ML features. Knowledge of data governance, security principles, and compliance
requirements
in healthcare (e. g. , HIPAA). Experience optimizing models for latency and throughput (e. g. , ONNX, model quantization). Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
We're removing barriers in the healthcare system to enable transparent, efficient, and cost-effective care delivery.
Our platform leverages cutting-edge technology, including AI, to address systemic challenges in healthcare access, pricing, and quality. Role OverviewWe are seeking a highly skilled and experienced AI Operations Engineer to join our engineering team.
This critical role is responsible evangelism for internal AI adoption as a force multiplier, as well as for building, deploying, and managing the end-to-end lifecycle of our machine learning models, which are central to optimizing our open marketplace and driving healthcare insights.
The ideal candidate will have an engineering background and bridge the gap between Data Science and Core Engineering, ensuring that our AI models are reliable, scalable, secure, and compliant in a high-stakes production environment.
Internal AI Force Multiplier: Educate and evangelize AI to boost productivity across all internal groups. Model Serving Deployment: Build scalable, high-availability models from claims, contracts, employer, employer and price transparency data to create intelligence and handle real-time inference requests for the OpenNetworks platform.
Infrastructure Management: Manage and optimize the underlying cloud infrastructure (compute, storage, and networking) for both large-scale model training and high-throughput serving environments. POC/Prototyping: Proficient in using rapid coding techniques to create POCs to demonstrate new AI capabilitiesModel Monitoring ReliabilityPerformance Monitoring: Implement advanced monitoring and alerting systems to track model performance, data drift, and prediction latency in production. Model Governance: Establish processes for model versioning, lineage tracking, and rollback capabilities to ensure operational integrity and auditability. Security Compliance: Ensure all MLOps practices and data handling adhere strictly to healthcare regulatory standards, including HIPAA and data privacy best practices. Collaboration OptimizationData Scientist Partnership: Collaborate directly with the Data Science team to containerize model code, optimize models for production serving efficiency, and transition models from research to production-readiness. Automation: Automate all aspects of the model lifecycle, from data ingestion and feature engineering to deployment and retraining triggers. Resource Efficiency: Continuously evaluate and implement technologies to improve the speed, cost-efficiency, and reliability of the overall ML system. Required
Qualifications
Experience: 3+ years of experience in MLOps, DevOps, or Software Engineering with a focus on machine learning systems. Programming: Expert proficiency in Python and solid experience with writing clean, production-level code. Cloud Containerization: Strong experience with a major cloud provider (AWS, GCP, or Azure) and expert knowledge of containerization technologies (Docker, Kubernetes). MLOps Tools: Hands-on experience with MLOps frameworks and platforms (e. g. , MLflow, Kubeflow, Sagemaker, TFX, or similar). Technical Foundation: Deep understanding of the machine learning lifecycle, from data prep and model training to deployment and monitoring. Preferred
Qualifications
Experience working in the HealthTech or FinTech industries, particularly with highly regulated data. Experience designing and managing data pipelines (ETL/ELT) for ML features. Knowledge of data governance, security principles, and compliance
requirements
in healthcare (e. g. , HIPAA). Experience optimizing models for latency and throughput (e. g. , ONNX, model quantization). Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
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Opennetworks United States
