Machine Learning Engineer

Work as a Remote Machine Learning Engineer supporting Cisco Learning’s opportunity-assessment solution. In this role, you will turn machine learning and analytical strategies into repeatable feature-engineering pipelines, end-to-end ML workflows, model lifecycle processes, and operational integrations that help models move reliably into real production environments. Based in St. Louis, MO with remote work supported, the role offers USD 60 to 65 per hour.

What you’ll do

• Convert ML and analytical strategies into feature-engineering pipelines, ML workflows, model lifecycle processes, and operational integrations.
• Build and maintain production-oriented ML solutions, including ML pipeline development, data preprocessing and transformation, and structured and unstructured data handling.
• Develop model training and evaluation workflows, followed by model inference support.
• Implement operational practices such as model registries, model versioning, experiment tracking, and model monitoring.
• Create automated testing and retraining workflows to keep models current and dependable.
• Support reproducible ML pipelines, including data ingestion/access pipelines and reliable cloud-based ML and data environments.
• Design APIs and/or downstream integrations and integrate models with an enterprise data environment.

Core skills and qualifications

• Strong Python and SQL.
• Hands-on with Pandas / NumPy (or comparable data-processing libraries).
• Experience processing structured and unstructured data.
• Knowledge of software development and version-control practices.
• Capability across the full ML engineering lifecycle: feature engineering, ML pipeline development, model training and evaluation, and model inference.
• Experience with Scikit-learn (or comparable ML frameworks).
• Competence in model registries, model versioning, experiment tracking, and model monitoring.
• Ability to implement automated testing and retraining workflows with a focus on reproducible ML pipelines.
• Experience with data ingestion/access pipelines, cloud-based ML/data environments, and APIs and downstream integrations.
• Familiarity with an enterprise data environment.

Tools you’ll use

• Python, SQL
• Pandas, NumPy
• Scikit-learn
• MLflow

Preferred background

• MLflow or comparable ML lifecycle tooling
• Feature store experience
• Containerization
• Cloud ML platforms
• API and integration development
• NLP/text-processing pipelines
• Document or vector ingestion
• Model inference and monitoring
• Automated ML testing and retraining
• Enterprise data-platform experience
• Production-oriented ML solutions
• Previous Cisco experience with the appropriate ML engineering skill set