Senior ML Engineer — Production-Ready Modeling & Pipelines

Apex Systems; Plano (TX); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Everforth Apex Systems is seeking a Senior Machine Learning Engineer to independently deliver complex ML models from data exploration to production deployment. You will work with large datasets, build data pipelines, and collaborate with engineering teams to integrate models into scalable workflows.

The ideal candidate has 5–10 years of hands-on ML experience, strong Python skills, and expertise in feature engineering, model training, evaluation, and production deployment within a hybrid

Qualifications

• Master’s or PhD in a quantitative/technical field is required.
• 5–10 years of hands-on data science or ML engineering experience delivering models/pipelines.
• Strong Python development with libraries like pandas, NumPy, scikit-learn, or XGBoost.

Responsibilities

• Design, train, validate, and tune supervised ML classification models.
• Perform data exploration, cleansing, transformation, and feature engineering.
• Develop Python-based workflows and pipelines for data prep, training, evaluation, and scoring.
• Evaluate model performance and iterate on quality, FP/FN analysis, and improvements.
• Package and deploy ML models into production or near-production with engineering teams.
• Troubleshoot data quality, pipelines, model performance, and integration issues.
• Create technical docs covering data assumptions, features, and model design.

Skills

Python

Pandas

NumPy

Scikit-learn

XGBoost

Data pipelines

Feature engineering

Production deployment

Model evaluation

Documentation

Generative AI tools

Education

Master’s or PhD in quantitative field

Tools

Spark

Databricks

AWS

Azure

SQL

Job description

Everforth Apex Systems is seeking a Senior Machine Learning Engineer to independently deliver complex ML models from data exploration to production deployment. You will work with large datasets, build data pipelines, and collaborate with engineering teams to integrate models into scalable workflows.

The ideal candidate has 5–10 years of hands-on ML experience, strong Python skills, and expertise in feature engineering, model training, evaluation, and production deployment within a hybrid