Agentic AI Engineer: LLMs, RAG & Autonomous Workflows

QUALCOMM, Inc.; San Diego (CA); Hybrid; USD 115,000 - 172,000; Full time

Job summary

Qualcomm, Inc. is seeking an experienced Machine Learning Engineer specializing in Agentic AI to design, develop, and deploy enterprise-grade AI systems powered by LLMs and autonomous agents.

You will focus on reasoning, planning, tool usage, and task execution within Hybrid AI architectures, leveraging RAG and memory-based learning for scalable business impact. The role emphasizes collaboration with cross-functional teams to implement safe, observable, and efficient agent workflows, with a

Qualifications

• Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field.
• Experience with LLM deployments and fine-tuning is preferred.
• Strong programming skills in Python and ML frameworks.

Responsibilities

• Design and implement Agentic AI systems capable of autonomous reasoning, planning, tool usage, and multi-step execution to solve enterprise problems.
• Build and optimize RAG, Hybrid AI, and knowledge-grounded architectures with enterprise data and tools.
• Develop self-improving AI agents using feedback loops, memory systems, and continual learning.
• Research LLM post-training techniques (SFT, RL, distillation, domain adaptation) for enterprise use cases.
• Create evaluation, benchmarking, observability, and safety frameworks for agent quality and impact.
• Build and maintain multi-agent orchestration frameworks for collaborative workflows.

Skills

Python

LLM frameworks

Prompt engineering

Reinforcement Learning

Vector databases

Education

Bachelors in CS/CE/EE

Tools

Langchain

LlamaIndex

Transformers

Docker

Kubernetes

Job description

Qualcomm, Inc. is seeking an experienced Machine Learning Engineer specializing in Agentic AI to design, develop, and deploy enterprise-grade AI systems powered by LLMs and autonomous agents.

You will focus on reasoning, planning, tool usage, and task execution within Hybrid AI architectures, leveraging RAG and memory-based learning for scalable business impact. The role emphasizes collaboration with cross-functional teams to implement safe, observable, and efficient agent workflows, with a