Applied AI Operations Engineer: LLM & Apps

United States Digital Space LLC; Redmond (WA); On-site; USD 100,000 - 165,000; Full time

Benefits offered by this job

Stock options

Long-term incentives

Bonuses

Medical, vision, and dental coverage

401(k) retirement plan

Paid vacation and holidays

Company shuttles

Job summary

United States Digital Space LLC in Redmond is hiring an Operations Engineer, Applied AI (Application Software) to partner with Sales, Product and Engineering. You will design practical strategies to integrate large language models into products and infrastructure with a focus on safe, responsible deployment.

This hybrid role blends strong technical depth with collaboration across teams to build prototypes and proof-of-concept applications, and to advise on architecture for LLM-powered solutions

Qualifications

• Bachelor’s degree in a STEM or engineering field.
• 1+ years of engineering or operations experience.
• Ability to design and prototype with LLMs and production systems.

Responsibilities

• Collaborate with internal teams to align technical approaches with goals.
• Serve as main technical contact, coordinating end-to-end efforts.
• Build prototypes and PoC apps to illustrate LLM integrations.
• Advise on system-on-system architecture for LLM-powered apps.
• Keep current on evolving LLM tech and best practices.

Skills

Cross-functional teamwork

Production code

LLM deployments

LLM techniques

AI product experience

Pressure management

AI integrations

Positive attitude

Education

Bachelor’s degree in STEM

Job description

United States Digital Space LLC in Redmond is hiring an Operations Engineer, Applied AI (Application Software) to partner with Sales, Product and Engineering. You will design practical strategies to integrate large language models into products and infrastructure with a focus on safe, responsible deployment.

This hybrid role blends strong technical depth with collaboration across teams to build prototypes and proof-of-concept applications, and to advise on architecture for LLM-powered solutions