Cloud ML Engineer: End-to-End Pipelines & LLMs

GCS Recruitment; Columbus (OH); On-site; USD 140,000 - 190,000; Full time

Job summary

GCS Recruitment Specialists is seeking a Machine Learning Engineer to join a high-performing AI and Data Science team responsible for designing and deploying predictive ML solutions in a cloud environment.

The role focuses on end-to-end pipelines using Python, PySpark, and MLOps practices, with opportunities to work on LLMs and scalable infrastructure within AWS.

Qualifications

• 5+ years of experience in Machine Learning Engineering or AI Engineering.
• Strong programming in Python.
• Hands-on experience with PySpark and large-scale data processing.
• Experience building ML models with Scikit-learn.
• Background in data analysis, feature engineering, and statistical modeling.
• Experience building and maintaining MLOps pipelines and deployment workflows.
• Experience working in AWS cloud environments.
• Familiarity with LLMs, NLP, or Generative AI.

Responsibilities

• Design, develop, and deploy machine learning models for predictive analytics and BI use cases.
• Build scalable data processing and feature engineering pipelines using PySpark.
• Develop, test, and optimize ML algorithms using Python and Scikit-learn.
• Implement end-to-end MLOps pipelines for training, deployment, monitoring, and lifecycle management.
• Work with large structured and unstructured datasets to derive insights and improve model performance.
• Collaborate with data scientists, software engineers, and business stakeholders to productionize ML solutions.
• Support integration of ML models into enterprise applications and workflows.
• Leverage AWS services to build scalable ML infrastructure.
• Explore and implement LLM solutions where applicable.
• Participate in model evaluation, performance tuning, and continuous improvement initiatives.

Skills

Python

PySpark

Scikit-learn

AWS

MLOps

LLMs

NLP

Generative AI

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)

GCS is acting as an Employment Business in relation to this vacancy.