Production‑Ready AI/ML Engineer (Full‑Stack)

Y99000 General Electric Company; Evendale (OH); Hybrid; USD 120,000 - 160,000; Full time

Job summary

General Electric Company is seeking an experienced AI/ML engineer with strong full‑stack capabilities to design, build, and maintain agentic workflows and autonomous systems. You will develop production‑grade AI models, collaborate across teams, and implement orchestration for multi‑agent tasks while ensuring scalable, maintainable code.

The role emphasizes staying current with AI/LLM advances, prompt engineering, and cloud deployments using AWS and containers.

Qualifications

• Bachelor’s degree in Computer Science or STEM majors.
• Minimum of 3 years in AI/ML engineering experience.
• Must not require or seek employment‑visa sponsorship from GE Aerospace.

Responsibilities

• Design, build, and maintain agentic workflows and autonomous systems.
• Develop and integrate AI models into production‑grade applications.
• Collaborate with cross‑functional teams to gather requirements and deliver AI‑driven solutions.
• Implement orchestration logic for multi‑agent systems and task automation.
• Write clean, scalable, and maintainable code in support of AI initiatives.
• Monitor and optimize the performance of AI systems in production.
• Stay current with advancements in AI, LLMs, and agentic architectures.

Skills

Full-stack development

AI/ML engineering

Python

AWS

LLMs

Docker/Kubernetes

Prompt engineering

Agile

Education

Bachelor’s Degree in Computer Science or STEM

Tools

AWS

Docker

Kubernetes

GitHub

Visual Studio

Job description

General Electric Company is seeking an experienced AI/ML engineer with strong full‑stack capabilities to design, build, and maintain agentic workflows and autonomous systems. You will develop production‑grade AI models, collaborate across teams, and implement orchestration for multi‑agent tasks while ensuring scalable, maintainable code.

The role emphasizes staying current with AI/LLM advances, prompt engineering, and cloud deployments using AWS and containers.