Generative AI Engineer – LLMs, RAG & Autonomy

Qualcomm; San Diego (CA); On-site; USD 115,000 - 172,000; Full time

Benefits offered by this job

Discretionary bonus program

Annual RSU grants

Competitive benefits package

Job summary

Qualcomm Technologies, Inc. is seeking an experienced Machine Learning Engineer specializing in Generative AI to join our core AI team in San Diego.

You will design, develop, and deploy cutting-edge generative AI solutions, focusing on LLMs, RAG, and intelligent agent systems. The role involves designing RAG-based solutions, optimizing LLM fine-tuning for domain adaptation, creating evaluation frameworks, and building agentic workflows for autonomous AI systems.

Qualifications

• Bachelor's degree in CS/CE/EE or related field.
• Experience with PyTorch or TensorFlow recommended.
• Familiar with LangChain, LlamaIndex, or Transformers.

Responsibilities

• Design and implement RAG-based solutions to enhance LLM capabilities with external knowledge sources
• Develop and optimize LLM fine-tuning strategies for specific use cases and domain adaptation
• Create robust evaluation frameworks for measuring and improving model performance
• Build and maintain agentic workflows for autonomous AI systems
• Collaborate with cross-functional teams to identify opportunities and implement AI solutions

Skills

Python

ML frameworks

PyTorch

TensorFlow

LangChain

LlamaIndex

Transformers

Prompt engineering

Containerization

MLOps

Reinforcement Learning

Open-source contributions

AI ethics

Education

Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field

Tools

LangChain

LlamaIndex

Transformers

Job description

Qualcomm Technologies, Inc. is seeking an experienced Machine Learning Engineer specializing in Generative AI to join our core AI team in San Diego.

You will design, develop, and deploy cutting-edge generative AI solutions, focusing on LLMs, RAG, and intelligent agent systems. The role involves designing RAG-based solutions, optimizing LLM fine-tuning for domain adaptation, creating evaluation frameworks, and building agentic workflows for autonomous AI systems.