MLOps Engineer

BrothersTech; United States; On-site; USD 120,000 - 190,000; Full time

Job summary

BrothersTech seeks an experienced MLOps Engineer to design, build, deploy, and maintain scalable ML infrastructure and production-grade ML pipelines.

The role emphasizes hands-on cloud platforms, ML deployment, CI/CD automation, containerization, and model monitoring to ensure robust, scalable systems.

Qualifications

• 3–5 years of experience in DevOps, ML Engineering, or MLOps.
• Hands-on Python programming and scripting skills.
• Experience deploying ML models into production.
• Experience with cloud infrastructure and Kubernetes.
• Understanding of ML lifecycle management.
• Experience building automated CI/CD and ML pipelines.
• Strong troubleshooting and problem-solving abilities.

Responsibilities

• Design, build, deploy, and maintain scalable ML infrastructure.
• Develop production-grade ML pipelines and model monitoring.
• Manage end-to-end ML lifecycle in cloud environments.
• Collaborate on CI/CD automation and containerization strategies.

Skills

Python

Scripting

ML deployment

Kubernetes

Cloud infrastructure

CI/CD

ML lifecycle management

Troubleshooting

Job description

Job Summary

We are looking for an experienced MLOps Engineer to design, build, deploy, and maintain scalable machine learning infrastructure and production-grade ML pipelines. The ideal candidate will have hands-on experience with cloud platforms, machine learning deployment, CI/CD automation, containerization, and model monitoring.

Required Qualifications

• 3–5 years of experience in DevOps, ML Engineering, or MLOps.
• Strong Python programming and scripting skills.
• Hands-on experience deploying ML models into production.
• Experience with cloud infrastructure and Kubernetes.
• Understanding of machine learning lifecycle management.
• Experience building automated CI/CD and ML pipelines.
• Strong troubleshooting and problem-solving abilities.

Preferred Qualifications

• Experience with LLM deployment and Generative AI infrastructure.
• Knowledge of model optimization and distributed training.
• Experience with feature stores and vector databases.
• Understanding of ML security and governance.