ML Engineer

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

Position Summary

We are seeking a Machine Learning Engineer to join a high-performing AI and Data Science team responsible for developing, deploying, and maintaining predictive machine learning solutions. This role will focus on designing end-to-end ML pipelines, building scalable predictive models, and operationalizing machine learning workloads in a cloud-based environment.

The ideal candidate will possess strong expertise in Python, PySpark, machine learning frameworks, data analysis, and MLOps practices. Experience working with Large Language Models (LLMs) and deploying machine learning solutions in AWS environments is highly desired.

Key Responsibilities

• Design, develop, and deploy machine learning models focused on predictive analytics and business intelligence use cases.
• Build scalable data processing and feature engineering pipelines using PySpark and distributed computing technologies.
• Develop, test, and optimize machine learning algorithms using Python and Scikit-learn.
• Implement and maintain end-to-end MLOps pipelines for model training, deployment, monitoring, and lifecycle management.
• Work with large structured and unstructured datasets to derive actionable insights and improve model performance.
• Collaborate with data scientists, software engineers, and business stakeholders to translate requirements into production-ready ML solutions.
• Support the integration of machine learning models into enterprise applications and workflows.
• Leverage AWS cloud services to build scalable and reliable machine learning infrastructure.
• Explore and implement Large Language Model (LLM) solutions where applicable.
• Participate in model evaluation, performance tuning, and continuous improvement initiatives.

Required Qualifications

• 5+ years of experience in Machine Learning Engineering, AI Engineering, or related disciplines.
• Strong programming expertise in Python.
• Hands-on experience with PySpark and large-scale data processing.
• Experience developing machine learning models using Scikit-learn.
• Strong background in data analysis, feature engineering, and statistical modeling.
• Experience building and maintaining MLOps pipelines and model deployment workflows.
• Knowledge of machine learning lifecycle management, CI/CD, and production model monitoring.
• Experience working within AWS cloud environments.
• Familiarity with Large Language Models (LLMs), NLP, or Generative AI technologies.
• Strong problem‑solving and communication skills.

Preferred Qualifications

• Experience developing Neural Network models using frameworks such as PyTorch or TensorFlow.
• Experience with workflow orchestration and pipeline management tools.
• Exposure to predictive AI solutions and advanced forecasting models.
• Knowledge of distributed machine learning and scalable AI infrastructure.
• Experience supporting enterprise‑scale machine learning deployments.

Technical Environment

• Python
• PySpark
• Scikit-learn
• AWS
• MLOps
• Large Language Models (LLMs)
• Machine Learning Pipelines
• Predictive Analytics
• Neural Networks (Preferred)
• PyTorch / TensorFlow (Preferred)