Remote Operations ML Engineer: Scalable AI Production

Jobgether SRL; United States; Remote; USD 140,000 - 163,000; Full time

Job summary

Jobgether SRL is seeking an Operations Machine Learning Engineer based in the United States. The role focuses on MLOps, cloud platform engineering, and data engineering to move AI from experimentation to production.

You will build automated pipelines, deployment frameworks, and monitoring to improve efficiency and business outcomes. You will collaborate with data scientists, engineers, and stakeholders to establish repeatable engineering practices, governance, and scalable ML deployments across

Qualifications

• Bachelor's degree in a quantitative/technical discipline.
• At least 3 years in ML engineering, MLOps, data engineering, or related field.
• Experience building production-grade data pipelines and ML workflows with Python.
• Strong knowledge of cloud data platforms (AWS, Databricks, Spark).
• Experience with CI/CD and deployment automation.

Responsibilities

• Operationalize ML models and AI solutions into scalable, production workflows.
• Design and maintain automated data and model pipelines.
• Develop reusable deployment patterns, CI/CD templates, and standards.
• Collaborate across data science, software, and cloud teams to align with enterprise architecture.

Skills

Python

Cloud platforms

MLOps

Data engineering

CI/CD

Monitoring

Responsible AI

Communication

Problem solving

ML deployment

Education

Bachelor's degree

Tools

AWS

Databricks

Apache Spark

CI/CD tools

Job description

Jobgether SRL is seeking an Operations Machine Learning Engineer based in the United States. The role focuses on MLOps, cloud platform engineering, and data engineering to move AI from experimentation to production.

You will build automated pipelines, deployment frameworks, and monitoring to improve efficiency and business outcomes. You will collaborate with data scientists, engineers, and stakeholders to establish repeatable engineering practices, governance, and scalable ML deployments across