Gen AI Transformation Engineer
Job in
San Diego, San Diego County, California, 92101, USA
Listed on 2026-09-03
Listing for:
Qualcomm
Full Time
position Listed on 2026-09-03
Job specializations:
-
Software Development
AI Engineer (Applied/Software), DevOps, Machine Learning/ ML Engineer, Python
Job Description & How to Apply Below
Qualcomm seeks a Gen AI Transformation Engineer to accelerate embedded software and engineering productivity for next gen wireless and media platforms. You will design and deploy GenAI solutions across firmware, embedded OS, and tool chains to automate workflows, optimize code, and enhance developer experience. Partner with software, silicon, and Dev Ops teams to integrate AI copilots, code analysis, and test generation into existing pipelines.
Evaluate models, build scalable inference services, measure impact on productivity, and drive best practices for secure, compliant AI adoption in a fast paced, innovation driven environment.
Responsibilities
- Design and implement Gen
- AI solutions to enhance embedded software development and engineering productivity
- Integrate AI copilots, code generation, and static analysis tools into existing CI/CD and development workflows
- Collaborate with embedded, firmware, tools, and Dev
- Ops teams to identify and automate high value engineering use cases
- Build and maintain scalable Gen
- AI services and APIs optimized for Qualcomm hardware and software stacks
- Evaluate and fine tune large language models and domain specific models for code and documentation tasks
- Develop metrics and dashboards to measure productivity, quality, and performance impact of AI solutions
- Ensure AI systems follow security, privacy, and responsible AI guidelines and Qualcomm standards
- Create technical documentation, best practices, and internal training for AI augmented engineering workflows
Required Skills
- Embedded software development
- C/C++ programming
- Python programming
- Machine learning and Gen
- AI (LLMs)
- Model integration and APIs (REST/GRPC)
- CI/CD pipelines and Dev
- Ops tooling
- Cloud platforms (AWS, Azure, or GCP)
- MLOps and model monitoring
- Software architecture and design
- Data engineering and feature pipelines
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