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Modernizing Medicine

Specialty-specific EHR systems for healthcare providers

Staff MLOps Engineer - ML Ops

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeBoca Raton, FL, USANo salary listed

About the Job

Requirements

  • At least 8 years of experience in DevOps, MLOps, Software Engineering, or Cloud Architecture, including delivering and operating AI/ML systems at scale.
  • Deep expertise in Python and software engineering principles for production-grade microservices, software development kits, and data pipelines.
  • Architectural experience across the full machine learning lifecycle, including training, batch and real-time inference, model registration, and application integration.
  • Proficiency with container orchestration using Kubernetes and EKS, serverless architectures using Lambda, and AWS cloud infrastructure.
  • Hands-on experience with machine learning and large language model frameworks such as MLflow, SageMaker, PyTorch, or Spark.
  • Mastery of Infrastructure as Code using Terraform and CI/CD and GitOps tooling.
  • Experience establishing enterprise observability standards using DataDog or similar platforms.
  • Leadership, communication, and cross-functional collaboration skills focused on developer enablement and mentoring.
  • Ability to participate in on-call support for production systems may be required.

Responsibilities

  • Architect, build, and operate a scalable developer enablement platform for deploying AI/ML models into high-throughput production applications and accelerate the end-to-end lifecycle of AI-enabled applications.
  • Define reusable patterns for model training, feature store integration, batch and real-time inference, and pipeline orchestration using Databricks, Airflow, and cloud-native services.
  • Ensure machine learning infrastructure uses standardized Infrastructure as Code for repeatable, multi-environment deployments across AWS accounts.
  • Lead the evolution of serverless and containerized machine learning execution environments, migrating high-scale serverless pipelines from AWS Lambda to Kubernetes (EKS) microservices where appropriate.
  • Drive CI/CD and GitOps standards for automated model delivery, champion deployment strategies, and automate infrastructure as code.
  • Design observability and monitoring capabilities using DataDog to track model drift, latency, error rates, and production system health.
  • Implement LLMOps and AIOps frameworks to manage, deploy, evaluate, and monitor large language models and generative AI solutions, leveraging MLflow for tracking, experiment management, and model registry.
  • Partner with Data Science, Product Development, and Security teams to ensure models and AI applications are secure, compliant, and cost-efficient.
  • Apply security best practices to protect sensitive data and ensure compliance.
  • Provide technical leadership and mentorship across MLOps and engineering teams, establishing best practices in system architecture and operational excellence.

Desired Qualifications

  • Experience with Databricks is a plus.

About the Company

Modernizing Medicine

Specialty-specific EHR systems for healthcare providers

Specialty-focused electronic health record (EHR) and related healthcare software. Its EMA and gGastro EHR systems run clinical documentation and integrate practice management features such as appointment scheduling, billing, and reporting, while learning from a provider’s workflows to adapt over time. This combination of adaptive EHR and revenue cycle/Practice Management tools differentiates Modernizing Medicine by offering an integrated, specialty-tailored solution rather than a generic system. The goal is to help healthcare providers spend more time with patients by reducing administrative work and improving financial performance.

Company size
1,001-5,000
Company stage
Late Stage VC
Total funding
$376.2M
Headquarters
Boca Raton, FL
Founded
2010

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