Senior GenAI Engineer - RAG & AI Systems

Honeywell Aerospace; Phoenix (AZ); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Employer-subsidized medical, dental,視覚

Short-term and long-term disability

401(k) match, FSAs & HSAs

Employee assistance program and教育援助

Parental leave and 12 paid holidays

Paid time off

Job summary

Honeywell Aerospace in Phoenix, AZ seeks an Advanced AI Engineer to provide technical leadership in designing and developing AI algorithms, models, and systems, and to guide methodologies and best practices.

You will collaborate with AI engineers, product managers, and aerospace experts to deliver on-premises AI deployments, including Retrieval Augmented Generation (RAG) solutions, with a focus on performance and reliability.

Qualifications

• Bachelor's degree in a technical discipline such as sciences/engineering/mathematics.
• Minimum 5 years of experience managing AI related technology projects.
• Mastery expertise in machine learning or related AI technology.

Responsibilities

• Work with AI engineers, product managers, and aerospace experts to understand requirements and develop AI applications that meet business needs.
• Perform research activities to identify state-of-the-art AI models and infrastructure tools for on-premises deployment.
• Ideate and develop Retrieval Augmented Generation (RAG) solutions for internal applications.

Skills

AI leadership

Machine learning expertise

AI research

Education

Bachelor's degree in a technical discipline (STEM)

Master's degree in ML/AI

Tools

APIs design & integration

Embeddings & indexing

UI/UX principles

KPI dashboards & visualization

On-premises AI deployment tooling

Database operations

Job description

Honeywell Aerospace in Phoenix, AZ seeks an Advanced AI Engineer to provide technical leadership in designing and developing AI algorithms, models, and systems, and to guide methodologies and best practices.

You will collaborate with AI engineers, product managers, and aerospace experts to deliver on-premises AI deployments, including Retrieval Augmented Generation (RAG) solutions, with a focus on performance and reliability.