Agentic AI Engineer – LLMs, RAG & Autonomous Systems

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

Job summary

Qualcomm Technologies, Inc. seeks a Machine Learning Engineer specializing in Agentic AI to design, develop, and deploy enterprise AI systems powered by LLMs, Agentic AI, Hybrid AI, RAG, and autonomous agents.

You will build solutions capable of reasoning, planning, tool usage, and workflow orchestration. The role focuses on integrating enterprise data, tools, and domain context, with emphasis on scalable architectures, safety, observability, and measurable business impact.

Qualifications

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field.
• Experience with LLM frameworks and AI deployment.
• Familiarity with enterprise data integration and tool usage.
• Strong fundamentals in Python and ML pipelines.

Responsibilities

• Design and implement Agentic AI systems for autonomous reasoning and multi-step execution.
• Build and optimize RAG, Hybrid AI, and knowledge-grounded architectures with enterprise data and tools.
• Develop self-improving agents using feedback, memory systems, and learning loops.
• Explore post-training techniques to improve enterprise AI performance.
• Establish evaluation, observability, and safety frameworks for agent quality and impact.
• Build multi-agent orchestration frameworks for collaborative workflows.

Skills

Python

ML frameworks

LLM deployment

Vector databases

Cloud platforms

RAG architectures

Agile

Communication

Team collaboration

Education

Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering

Tools

Langchain

LlamaIndex

Transformers

Job description

Qualcomm Technologies, Inc. seeks a Machine Learning Engineer specializing in Agentic AI to design, develop, and deploy enterprise AI systems powered by LLMs, Agentic AI, Hybrid AI, RAG, and autonomous agents.

You will build solutions capable of reasoning, planning, tool usage, and workflow orchestration. The role focuses on integrating enterprise data, tools, and domain context, with emphasis on scalable architectures, safety, observability, and measurable business impact.