AI Engineering Operations Specialist

Qualcomm; San Diego (CA); On-site; USD 110,000 - 166,000; Full time

Job summary

Qualcomm Technologies, Inc. seeks an AI Engineering Operations Analyst to drive planning, coordination, and execution of data center AI initiatives within Engineering Operations. You will track progress, organize information, and prepare executive-ready materials for leadership.

The role emphasizes hands-on analytics, development of repeatable processes using AI tools, and collaboration with engineering, IT, product, and operations teams to scale AI workflows across the organization.

Qualifications

• Experience in program analysis, operations, process improvement, project coordination, or technical program support.
• Strong interest in AI and technology with ability to follow technical discussions.
• Excellent organizational skills and strong attention to detail.
• Strong written, verbal, and visual communication, including creating executive slide decks.

Responsibilities

• Develop plans, schedules, and timelines for internal AI projects.
• Track milestones, risks, and deliverables for Engineering leadership.
• Consolidate weekly status reports and produce executive-ready summaries.
• Coordinate demos and maintain AI content repositories and materials.
• Create lightweight automation and analytics solutions to standardize processes.

Skills

Program analysis

Operations

Project coordination

Technical program support

PowerPoint presentations

Education

Bachelor's degree in Business, Engineering, Science, or related field

Associate's degree and 2+ years of relevant work experience

High School Diploma or equivalent and 4+ years of relevant work experience

Tools

PowerPoint

Job description

Qualcomm Technologies, Inc. seeks an AI Engineering Operations Analyst to drive planning, coordination, and execution of data center AI initiatives within Engineering Operations. You will track progress, organize information, and prepare executive-ready materials for leadership.

The role emphasizes hands-on analytics, development of repeatable processes using AI tools, and collaboration with engineering, IT, product, and operations teams to scale AI workflows across the organization.