Machine Learning Engineer (AWS SageMaker)

TL;DR

Machine Learning Engineer (AWS SageMaker): Building and optimizing robust machine learning systems for production environments with an accent on scalable model development, deployment, and MLOps practices. Focus on designing, developing, and deploying ML models, maintaining ML pipelines, and implementing CI/CD workflows.

Location: Charlotte, NC

Company

hirify.global is seeking a Machine Learning Engineer / AI Specialist to join a dynamic and fast-evolving data science team.

What you will do

• Design, develop, and deploy machine learning models using AWS SageMaker.
• Build and maintain ML pipelines for model training, validation, and deployment.
• Implement MLOps best practices, including CI/CD workflows for model lifecycle automation.
• Collaborate closely with data scientists to productionize research models.
• Monitor and optimize model performance, cost, and reliability.
• Debug and maintain Terraform and Concourse pipelines, and migrate repositories to GitHub.

Requirements

• Bachelor’s degree in Computer Science, Data Science, Engineering or 8+ years equivalent experience.
• 3+ years of experience in machine learning engineering, AI development, or data science operations.
• Strong Python programming skills, including NumPy, Pandas, and Scikit-learn.
• Hands-on experience with AWS SageMaker for training, tuning, and deploying models.
• Solid background in data science methodologies, statistical analysis, and software engineering best practices.
• Deep understanding of MLOps, containerization (Docker, Kubernetes), and CI/CD pipelines.
• Experience with Infrastructure-as-Code tools (Terraform, CloudFormation) and AWS services (S3, EC2, Lambda, CloudWatch).

Nice to have

• Master’s degree in a relevant technical field and AWS Certifications.
• Experience with monitoring tools (Prometheus, Grafana) and big data frameworks (EMR, Spark, Hadoop).
• Strong SQL expertise and experience with ETL tools (SSIS, Sqoop, Spark).