Machine Learning Ops Engineer

Seniorlink Incorporated d/b/a Careforth; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

Seniorlink Incorporated d/b/a Careforth is seeking a full-time Machine Learning Ops Engineer to design and maintain scalable infrastructure for ML models, with a focus on automated pipelines, model serving, and HIPAA compliance.

You will design automated ML pipelines using MLflow and AWS SageMaker, build scalable serving infrastructure with Docker and Kubernetes for real-time and batch scoring, and implement CI/CD and production monitoring.

Qualifications

• Bachelor's or Master's degree in computer science, software engineering, or a related technical field.
• 7+ years in DevOps, Data Engineering, or ML Engineering, with at least 4 years in ML operations.
• Experience operating production ML systems, including model serving and lifecycle governance.
• Expert-level containerization/orchestration with Docker and Kubernetes/EKS.
• Strong proficiency with AWS services for ML and data processing, HIPAA aware.

Responsibilities

• Design and implement automated ML pipelines for training, evaluation, and deployment using MLflow and AWS SageMaker.
• Build and manage scalable model serving infrastructure using Docker and Kubernetes for real-time and batch scoring.
• Establish CI/CD workflows and production monitoring for data drift, model decay, and pipeline failures.

Skills

Production ML

ML Ops

Cloud & DevOps

Security & HIPAA

Education

Bachelor's or Master's in CS/SE

Tools

Docker

Kubernetes/EKS

MLflow

AWS SageMaker

CI/CD

Monitoring & Observability

Job description

To support the Product & Technology Organization, the full-time Machine Learning Ops Engineer will design and maintain infrastructure for scaling ML models, focusing on automated pipelines, model serving, and compliance with HIPAA regulations.

Key responsibilities

• Design and implement automated ML pipelines for model training, evaluation, and deployment using MLflow and AWS SageMaker
• Build and manage scalable model serving infrastructure using Docker and Kubernetes for real-time and batch scoring
• Establish CI/CD workflows and production monitoring for data drift, model decay, and pipeline failures

Required qualifications

• Bachelor's or Master's Degree in Computer Science, Software Engineering, or a related technical field
• 7+ years of professional experience in DevOps, Data Engineering, or ML Engineering, with at least 4 years focused on Machine Learning operations
• Proven experience in operating production ML systems, including model serving and lifecycle governance
• Expert-level skills in containerization and orchestration with Docker and Kubernetes/EKS
• Strong proficiency in AWS services relevant to machine learning and data processing