Generative AI ML Engineer — LLMs, RAG & Autonomous Agents

Latitude; San Diego, Northern (CA, KY); Hybrid; USD 104,000 - 156,000; Full time

Job summary

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

The role focuses on designing, developing, and deploying cutting-edge generative AI solutions, with emphasis on LLMs, RAG, and intelligent agent systems. You will design RAG-based knowledge integration, fine-tune LLMs for domain-specific tasks, and build evaluation and deployment pipelines with ML ops best practices.

Qualifications

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field.
• Experience with AI/ML frameworks and model deployment is preferred.

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

PyTorch

TensorFlow

LLM deployments

Vector databases

Cloud platforms

RAG architectures

Communication skills

Agile

Problem solving

Education

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

Tools

Langchain

LlamaIndex

Transformers

Containerization

Microservices

ML Ops

Model deployment pipelines

Job description

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

The role focuses on designing, developing, and deploying cutting-edge generative AI solutions, with emphasis on LLMs, RAG, and intelligent agent systems. You will design RAG-based knowledge integration, fine-tune LLMs for domain-specific tasks, and build evaluation and deployment pipelines with ML ops best practices.