Remote ML Engineer – Production-Ready Models & Deployment

Bright Vision Technologies; Frisco (TX); Remote; USD 115,000 - 165,000; Full time

Job summary

Bright Vision Technologies is seeking a Machine Learning Engineer to develop, train, evaluate, and operationalize predictive models for production applications. You will work across data preparation, feature engineering, model experimentation, validation, and deployment with data scientists and software teams.

Key responsibilities include preparing datasets, building reproducible training pipelines, developing models for classification, regression, ranking or forecasting, and integrating

Qualifications

• 5+ years in machine learning engineering, applied data science, or related production model development.
• Strong Python skills with libraries such as scikit-learn, pandas, NumPy.
• Experience with PyTorch or TensorFlow for relevant projects.
• Experience building reproducible data and model pipelines and managing code via Git.
• Ability to deploy or integrate model inference into APIs, batch workflows, or services.

Responsibilities

• Prepare datasets, engineer features, and build reproducible training and validation pipelines.
• Develop, tune, and evaluate models for classification, regression, ranking, or forecasting use cases.
• Implement model-serving components and integrate inference workflows with platforms.
• Track model performance, investigate drift or quality degradation, and recommend retraining actions.
• Document experiments, model assumptions, evaluation results, and decisions for engineering stakeholders.

Skills

Python

scikit-learn

pandas

NumPy

PyTorch

TensorFlow

Git

API deployment

Model evaluation

Feature engineering

Tools

Git

Docker

Kubernetes

Job description

Bright Vision Technologies is seeking a Machine Learning Engineer to develop, train, evaluate, and operationalize predictive models for production applications. You will work across data preparation, feature engineering, model experimentation, validation, and deployment with data scientists and software teams.

Key responsibilities include preparing datasets, building reproducible training pipelines, developing models for classification, regression, ranking or forecasting, and integrating