AI/ML Engineer, Production AI & LLM Apps (Remote)

GE Aerospace; Olde West Chester (OH); Remote; USD 112,000 - 150,000; Full time

Benefits offered by this job

Health insurance

401(k) with match

Tuition assistance

Paid time off

Job summary

GE Aerospace in the United States is seeking an AI Engineer to transform operational data into production-grade ML pipelines, models, and LLM-powered applications. You'll design, build, and maintain AI/ML products, train models, develop APIs, and collaborate with analytics teams to embed AI in existing tools for real-time decision support.

This role covers full lifecycle from requirements to deployment, emphasizes responsible AI, model monitoring, and scalable data pipelines using Databricks and

Qualifications

• Bachelor's degree in CS/DS/Engineering from an accredited program (required).
• Minimum of 3 years hands-on AI/ML engineering experience delivering production models and AI-powered applications.
• Experience with AWS, Databricks, and building production-ready ML systems.

Responsibilities

• Design, build, and maintain AI/ML products including LLM-powered applications and forecasting models.
• Own full AI/ML lifecycle from requirements analysis to deployment and operational support.
• Develop data pipelines and APIs to enable real-time decision support.

Skills

AI/ML engineering

Prompt engineering

Model deployment

Python

REST APIs

AWS

Databricks

MLOps

Education

Bachelor's Degree in Computer Science / Data Science / Engineering

Tools

MLflow

Weights & Biases

FastAPI

Databricks

GitHub

Job description

GE Aerospace in the United States is seeking an AI Engineer to transform operational data into production-grade ML pipelines, models, and LLM-powered applications. You'll design, build, and maintain AI/ML products, train models, develop APIs, and collaborate with analytics teams to embed AI in existing tools for real-time decision support.

This role covers full lifecycle from requirements to deployment, emphasizes responsible AI, model monitoring, and scalable data pipelines using Databricks and