Senior ML Engineer - Production Predictive Modeling

GCS Recruitment; Baltimore (MD); On-site; USD 140,000 - 190,000; Full time

Job summary

GCS Recruitment is seeking a Senior Machine Learning Engineer to join a team building predictive models for construction and operations. You’ll own production ML models, analyze performance, and engineer features from historical and geographic data to improve accuracy.

The role emphasizes retraining pipelines, exploring new predictive problems, and collaborating with engineers to productionize ML solutions in an AWS-based environment with modern tooling.

Qualifications

• Strong ML engineering background with production experience.
• Experience building predictive models and improving accuracy.
• Proficient in Python and ML tooling (MLflow, DVC).

Responsibilities

• Own and enhance production machine learning models.
• Analyze model performance and identify areas for improvement.
• Develop feature engineering strategies using historical/geographic data.
• Build automated retraining pipelines.
• Develop predictive models using algorithms such as XGBoost.
• Work with time-based and historical trends to improve predictions.
• Evaluate factors like county, ZIP code, job type, seasonality, holidays, and processing times.
• Develop models for construction duration and permitting.
• Contribute to predictive cost modeling with historical labor/data.
• Explore ML approaches for automating construction design.
• Collaborate with engineers to productionize ML solutions.

Skills

Python

XGBoost

ML pipelines

AWS

Feature engineering

ML engineering

ML tooling

Tools

MLflow

DVC

HashiCorp Nomad

FastAPI

Job description

GCS Recruitment is seeking a Senior Machine Learning Engineer to join a team building predictive models for construction and operations. You’ll own production ML models, analyze performance, and engineer features from historical and geographic data to improve accuracy.

The role emphasizes retraining pipelines, exploring new predictive problems, and collaborating with engineers to productionize ML solutions in an AWS-based environment with modern tooling.