ML Engineer

GCS Recruitment; Philadelphia (Philadelphia County); On-site; USD 120,000 - 160,000; Full time

Job summary

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

The ideal candidate has strong Python, PySpark, Scikit-learn expertise, and experience with AWS and MLOps. Familiarity with LLMs/NLP and deployment in enterprise systems is highly desired.

Qualifications

• 5+ years of experience in Machine Learning Engineering or AI engineering.
• Strong programming experience in Python.
• Hands-on PySpark and large-scale data processing.
• Experience developing ML models with Scikit-learn.
• Experience building MLOps pipelines, deployment, and monitoring.
• Familiarity with AWS cloud environments and LLMs/NLP.

Responsibilities

• Design, develop, and deploy ML models for predictive analytics and BI use cases.
• Build scalable data processing and feature pipelines with PySpark.
• Develop, test, and optimize ML algorithms using Python and Scikit-learn.
• Implement end-to-end MLOps pipelines for training, deployment, and monitoring.
• Collaborate with data scientists, engineers, and stakeholders to productionize ML solutions.
• Leverage AWS to build scalable ML infrastructure and enable LLM integration.

Skills

Python

PySpark

Scikit-learn

AWS

MLOps

LLMs / NLP

CI/CD

PyTorch

TensorFlow

Tools

CI/CD

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)