Staff Cloud Engineer, ML Ops

ModMed•Boca Raton, FL

2d•Hybrid

About The Position

As a Staff MLOps Engineer at Modernizing Medicine, you will bridge data science, machine learning engineering, and production systems. You will establish architecture and tooling that enable developers to build, deploy, and operate secure, reliable AI-enabled applications at scale. The role drives architectural patterns for model training, deployment, and lifecycle management and builds self-service capabilities across product and platform teams. Your work will shape the enterprise platform for future AI-driven clinical and operational products. The position focuses on developing and operating a scalable developer enablement platform for production AI/ML workloads, defining reusable ML patterns, standardizing infrastructure as code across AWS accounts, and evolving serverless and Kubernetes execution environments. You will also lead CI/CD and GitOps practices, design observability and monitoring, implement LLMOps and AIOps frameworks, and partner with data science, product development, and security teams to deliver secure, compliant, and cost-efficient systems. The role includes technical leadership and mentorship. It is hybrid, and on-call participation may be required to support production systems.

Requirements

At least 8 years of experience in DevOps, MLOps, Software Engineering, or Cloud Architecture, including proven experience delivering and operating AI/ML systems at scale.
Deep expertise in Python and software engineering principles for production-grade microservices, SDKs, and data pipelines.
Architectural experience with patterns across the full ML lifecycle, including training, batch and real-time inference, model registration, and application integration.
Strong proficiency with Kubernetes/EKS, serverless architectures such as Lambda, and AWS cloud infrastructure.
Mastery of Infrastructure as Code, specifically Terraform, and CI/CD/GitOps tooling.
Demonstrated ability to establish enterprise observability standards using DataDog or similar platforms.
Strong leadership, communication, and cross-functional collaboration skills, with a focus on developer enablement and mentoring.

Nice To Haves

Hands-on experience with ML and LLM frameworks such as MLflow, SageMaker, PyTorch, or Spark.
Experience with Databricks.

Responsibilities

Architect, build, and operate a scalable developer enablement platform for deploying AI/ML models in high-throughput production 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.
Standardize ML infrastructure as code to support repeatable, multi-environment deployments across AWS accounts.
Lead the evolution of serverless and containerized ML execution environments, including migrating high-scale AWS Lambda pipelines to Kubernetes (EKS) microservices where appropriate.
Drive CI/CD and GitOps standards for automated model delivery, deployment strategies, and infrastructure-as-code automation.
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 LLMs and generative AI solutions, using 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.

Benefits

Comprehensive medical, dental, and vision benefits, including a company Health Savings Account contribution.
401(k) matching contribution of 50% of employee contributions deferred on up to 6% of compensation; matching contributions vest after one year of employment.
Paid time off.
Paid parental leave.
Company-paid life and disability benefits.
Flexible Spending Account.
Employee Assistance Programs.
Company-sponsored Business Resource and Special Interest Groups.
Professional development opportunities, including tuition reimbursement and unlimited access to LinkedIn Learning.
In-person collaboration opportunities and access to a dog-friendly U.S. headquarters; hybrid office-based roles and remote availability for some roles.
Weekly catered breakfast and lunch, treadmill workstations, Zen, and wellness rooms at the BRIC headquarters.