Senior ML Engineer — Cloud, MLOps & LLMs

GCS Recruitment; Reston (VA); On-site; USD 120,000 - 180,000; Full time

Job summary

GCS Recruitment is seeking a Machine Learning Engineer to join a high-performing AI and Data Science team in Reston, VA. You will design end-to-end ML pipelines, build scalable predictive models, and operationalize ML workloads in a cloud environment.

The role emphasizes Python, PySpark, Scikit-learn, MLOps, and experience with AWS and LLMs. You will collaborate with data scientists and engineers to deploy production-ready ML solutions and continuously improve model performance.

Qualifications

• 5+ years in ML engineering or related roles.
• Strong Python programming.
• Hands-on PySpark and large-scale data processing experience.
• Experience with Scikit-learn and ML pipelines.
• Experience with AWS and cloud ML deployments.
• Familiar with LLMs / NLP and Generative AI.

Responsibilities

• Design, develop, and deploy ML models for predictive analytics and BI use cases.
• Build scalable data processing and feature engineering pipelines with PySpark.
• Develop, test, and optimize ML algorithms using Python and Scikit-learn.
• Implement and maintain end-to-end MLOps pipelines for training, deployment, monitoring, and lifecycle management.
• Collaborate with data scientists, software engineers, and stakeholders to translate requirements into production ML solutions.
• Leverage AWS to build scalable ML infrastructure and deploy models into production.

Skills

Python

PySpark

Scikit-learn

MLOps

AWS

LLMs

Data analysis

Statistical modeling

CI/CD

Communication skills

Tools

PyTorch

TensorFlow

Job description

GCS Recruitment is seeking a Machine Learning Engineer to join a high-performing AI and Data Science team in Reston, VA. You will design end-to-end ML pipelines, build scalable predictive models, and operationalize ML workloads in a cloud environment.

The role emphasizes Python, PySpark, Scikit-learn, MLOps, and experience with AWS and LLMs. You will collaborate with data scientists and engineers to deploy production-ready ML solutions and continuously improve model performance.