Staff ML Engineer: Scalable Healthcare MLOps Architect

Monogram Health; Northern (KY); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Medical, dental, and vision insurance

Employee assistance program

Life insurance

Disability insurance

401k with employer match

Paid holidays

Flexible vacation time

Paid parental leave

Wellness programs

Job summary

Monogram Health is seeking a Staff Engineer, Machine Learning Operations to architect, own, and scale the ML infrastructure and deployment pipelines powering clinical initiatives. You will lead production-grade systems with autonomy, balancing innovation with reliability to support patient outcomes.

The role requires deep expertise in MLOps tooling, cloud deployment, containerization, CI/CD, and model monitoring; you will mentor teams, drive strategic decisions, and collaborate with clinical

Qualifications

• Bachelor’s degree in computer science, engineering, or related field; master’s degree preferred.
• 10+ years in software engineering with 5+ years focused on ML infrastructure, MLOps, or production ML systems and Python development; 3+ years deploying production ML on cloud platforms (Azure preferred).
• Proven track record building and scaling ML platforms from the ground up.
• Healthcare or regulated industry experience strongly preferred.
• Expert-level proficiency with MLOps tooling (MLflow, Kubeflow, SageMaker, Azure ML etc.).
• Deep experience with containerization (Docker, Kubernetes), orchestration tools (Airflow, Prefect), and infrastructure-as-code (Terraform, ARM templates).
• Advanced knowledge of CI/CD systems, automated testing strategies, and GitOps workflows.
• Expertise in model monitoring, observability, feature stores, and experiment tracking at scale.
• Production experience with both batch and real-time inference architectures.
• Understanding of healthcare data standards (FHIR, HL7, claims data) is a plus.
• Demonstrated ability to influence technical direction and mentor senior engineers.
• Proven communication skills with ability to distill complex technical concepts for diverse audiences.
• Track record of driving consensus on architectural decisions across multiple stakeholders.
• Systems thinking skills with focus on reliability, scalability, and maintainability preferred.
• Understanding of security, compliance, and privacy requirements in healthcare (HIPAA) preferred.

Responsibilities

• Architect and maintain enterprise-grade ML infrastructure, including model versioning, automated testing frameworks, containerization strategies, CI/CD pipelines, and comprehensive monitoring systems for model performance, data quality, and drift detection.
• Drive MLOps strategy and standards across the organization. Mentor data scientists and engineers on production best practices, system design, and scalable architecture patterns.
• Own the complete journey from model development through production deployment, including real-time and batch inference systems, A/B testing frameworks, and automated retraining pipelines.
• Collaborate with clinical leaders, product teams, and data scientists to translate complex healthcare requirements into robust, scalable ML solutions. Present technical strategies to executive stakeholders.
• Build fault-tolerant, compliant systems that meet healthcare security and privacy standards. Establish SLAs, incident response protocols, and disaster recovery procedures for mission-critical ML services.
• Evaluate and integrate cutting-edge MLOps tools and practices. Design systems that scale with Monogram's growth while reducing operational overhead and improving model iteration velocity.

Skills

MLOps

Python development

Cloud platforms

Docker

Kubernetes

CI/CD

GitOps

Model monitoring

Leadership

Education

Bachelor's degree

Master's preferred

Tools

MLflow

Kubeflow

SageMaker

Azure ML

Terraform

Airflow

Job description

Monogram Health is seeking a Staff Engineer, Machine Learning Operations to architect, own, and scale the ML infrastructure and deployment pipelines powering clinical initiatives. You will lead production-grade systems with autonomy, balancing innovation with reliability to support patient outcomes.

The role requires deep expertise in MLOps tooling, cloud deployment, containerization, CI/CD, and model monitoring; you will mentor teams, drive strategic decisions, and collaborate with clinical