AI/ML Engineer — Production Pipelines & LLM Apps

GE Aerospace; Omaha (NE); Remoto; USD 112.000 - 150.000; Tempo pieno

Vantaggi offerti da questo lavoro

Health benefits

401(k) plan

Tuition assistance

Paid time off

Descrizione del lavoro

GE Aerospace's CES BI team seeks an AI Engineer to transform operational data into production-grade ML pipelines, models, and LLM-powered apps. You will design, train, and deploy AI/ML products and APIs, partnering with analytics teams to embed AI in existing tools.

This role covers lifecycle: requirements, model design, training, evaluation, deployment, and monitoring, with collaboration across teams to drive measurable business impact from real-time decisions.

Competenze

• Bachelor's Degree in CS/DS/Engineering or related field from an accredited college or university
• Minimum of 3 years of hands-on AI/ML engineering experience building and deploying ML models and/or AI-powered applications to production

Mansioni

• Define, build, and evolve AI-powered software products that accelerate Commercial Engine Services operations—including LLM applications, ML models, and intelligent automation for supply chain optimization
• Package AI/ML models as robust, well-documented APIs for dashboards and workflows
• Collaborate with BI team to embed AI features in existing applications with natural language queries and predictive insights
• Provide hands-on AI/ML technical leadership for modernization, including prompt engineering and model evaluation

Conoscenze

Python

AI/ML engineering

LLM applications

AWS

Databricks

MLOps

REST APIs

Formazione

Bachelor's degree in Computer Science, Data Science, Engineering

Strumenti

FastAPI

Flask

MLflow

Weights & Biases

CI/CD

GitHub

Descrizione del lavoro

GE Aerospace's CES BI team seeks an AI Engineer to transform operational data into production-grade ML pipelines, models, and LLM-powered apps. You will design, train, and deploy AI/ML products and APIs, partnering with analytics teams to embed AI in existing tools.

This role covers lifecycle: requirements, model design, training, evaluation, deployment, and monitoring, with collaboration across teams to drive measurable business impact from real-time decisions.