Staff MLOps Engineer: AI-Driven Cloud Platform

Modernizing Medicine; Boca Raton (FL); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Comprehensive benefits

Job summary

Modernizing Medicine is seeking a Staff MLOps Engineer to bridge data science, machine learning engineering, and production systems. You will lead architecture and tooling for fast, reliable, and secure AI-enabled applications at scale, empowering self-service capabilities for product and platform teams.

You will drive model training, deployment, and lifecycle management, partnering with Data Science, Product, and Security to ensure secure, compliant, and cost-efficient solutions.

Qualifications

• 8+ years of experience in DevOps, MLOps, Software Engineering, or Cloud Architecture.
• Strong Python and production-grade software engineering skills.
• Experience with Kubernetes/EKS, Lambda, and AWS infrastructure.
• Experience with MLflow, SageMaker, PyTorch, or Spark; Databricks a plus.

Responsibilities

• Architect and operate a scalable developer enablement platform for deploying AI/ML models into production applications.
• Define reusable patterns for model training, feature store integration, inference, and pipelines using Databricks, Airflow, and cloud services.
• Ensure ML infrastructure uses IaC for repeatable deployments across AWS accounts.
• Lead evolution of serverless and containerized ML execution environments; migrate Lambda-based pipelines to Kubernetes where appropriate.
• Drive CI/CD and GitOps standards for automated model delivery and infra automation.
• Design observability and monitoring with DataDog to track model drift, latency, and health.

Skills

Python

Microservices

Kubernetes

AWS

MLflow

SageMaker

PyTorch

Terraform

CI/CD

DataDog

Leadership

Job description

Modernizing Medicine is seeking a Staff MLOps Engineer to bridge data science, machine learning engineering, and production systems. You will lead architecture and tooling for fast, reliable, and secure AI-enabled applications at scale, empowering self-service capabilities for product and platform teams.

You will drive model training, deployment, and lifecycle management, partnering with Data Science, Product, and Security to ensure secure, compliant, and cost-efficient solutions.