Machine Learning Engineer

GCS Recruitment; Longview (TX); Presencial; USD 120.000 - 180.000; Jornada completa

Descripción de la vacante

GCS Recruitment in Texas is seeking a hands-on ML Engineer to join our data team. You will own production ML models used for forecasting project timelines, permit approvals, demand, and operational risks, and you will work with Python, SQL, Spark, and cloud tooling to deliver scalable predictions.

If you enjoy feature engineering, model optimization, and deploying end-to-end ML solutions in a modern cloud environment, this role offers ownership, collaboration with ML engineers, and impact across

Formación

• Strong Python and SQL proficiency for ML pipelines.
• Experience with forecasting and time-series modeling.
• Experience deploying production ML models.
• Familiarity with Spark ecosystem and ML tooling.

Responsabilidades

• Own production ML models and forecast accuracy improvements.
• Engineer new features and optimize model performance.
• Deploy scalable ML solutions to cloud environments.
• Collaborate with data scientists and engineers to maintain production ML systems.

Conocimientos

Python

SQL

Machine Learning

Forecasting

Time-Series

Feature Engineering

Model Deployment

Spark

MLflow

Docker

Kubernetes

Cloud (AWS/Azure)

Production ML

Herramientas

XGBoost

Scikit-Learn

LightGBM

PySpark

MLflow

Docker

Kubernetes

AWS

Azure

Descripción del empleo

Role Overview

We're working with a team that uses Machine Learning and Predictive Analytics to forecast project timelines, permit approvals, resource planning, customer demand, and operational risks. These predictions help drive key business decisions across marketing, sales, and operations.

The team already has machine learning models in production and is looking for someone who can take ownership of existing models, improve prediction accuracy, engineer new features, optimize performance, and deploy scalable ML solutions.

Required Skills

• Strong Python and SQL experience
• Machine Learning and Predictive Analytics
• XGBoost, Scikit-Learn, LightGBM, or similar algorithms
• Forecasting and Time-Series Modeling
• Feature Engineering and Model Optimization
• Model Evaluation (Precision, Recall, F1, ROC-AUC, MAE, RMSE)
• PySpark / Apache Spark
• MLflow, Docker, Kubernetes
• AWS or Azure Cloud
• Experience deploying and maintaining production ML models

Preferred Background

• Demand Forecasting
• Customer Churn Prediction
• Revenue Forecasting
• Risk Scoring
• Resource Planning
• Pricing Optimization
• Capacity Planning
• Supply Chain Analytics
• Operational Forecasting