Enterprise Agentic AI Engineer: LLMs, RAG & Autonomy

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

Job summary

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

The role focuses on autonomous reasoning, tool usage, planning, memory, and multi-agent orchestration to deliver scalable, production-ready AI solutions with measurable business impact. Collaborate with cross-functional teams to push post-training techniques, RAG architectures, model

Qualifications

• Bachelor's degree in a related field.
• Hands-on experience with LLM deployments and AI systems.
• Knowledge of prompt engineering and chain-of-thought techniques.
• Experience with ML ops and model deployment pipelines.

Responsibilities

• Design and implement Agentic AI systems with autonomous reasoning and multi-step execution.
• Build and optimize RAG, Hybrid AI, and knowledge-grounded architectures.
• Develop self-improving agents using feedback loops and reinforcement learning.
• Research LLM post-training techniques and domain adaptation.
• Create evaluation, observability, and safety frameworks for agent quality.

Skills

Python

PyTorch

TensorFlow

LLM deployments

RAG

Vector databases

Cloud platforms

Agile methodologies

Reinforcement learning

Team collaboration

Education

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

Tools

Langchain

LlamaIndex

Transformers

Docker/Kubernetes

Job description

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

The role focuses on autonomous reasoning, tool usage, planning, memory, and multi-agent orchestration to deliver scalable, production-ready AI solutions with measurable business impact. Collaborate with cross-functional teams to push post-training techniques, RAG architectures, model