Generative AI Engineer II (LLM/NLP)

Cisco Systems, Inc.; San Francisco (CA); On-site; USD 44,000 - 130,000; Full time

Job summary

Cisco Systems, Inc. in San Francisco invites students to join the engineering team building next-generation AI/ML solutions. You will collaborate with platform, security, release engineering, and support teams to deliver high-impact generative AI applications and ensure their reliable operation.

The internship emphasizes hands-on modeling, data prep, and production-ready code, with mentoring and opportunities to learn about LLM optimization, GPU techniques, and scalable architectures.

Qualifications

• Pursuing undergraduate or master's in CS, EE, data science, ML, AI, statistics, math, software engineering, or related field.
• Backend development skills in Go or Python demonstrated via a technical assessment or code sample.
• Understanding of LLM infrastructure and optimization strategies demonstrated in interview or documentation.
• Experience with model building and AI/LLM tasks evidenced by portfolio or projects.

Responsibilities

• Collaborate with engineers to prep data, design custom layers, and automate model deployment.
• Develop and deploy production-ready AI models and solutions.
• Experiment with new technologies and maintain robust testing and scalable workflows.

Skills

Go

Python

LLM infra

Model building

Code samples

Cybersecurity basics

Distributed systems

GPU optimization

Cloud platforms

Education

Undergraduate degree (CS/EE/Data Science)

Master's degree (CS/AI/ML)

Tools

vLLM

Triton

TorchServe

Job description

Cisco Systems, Inc. in San Francisco invites students to join the engineering team building next-generation AI/ML solutions. You will collaborate with platform, security, release engineering, and support teams to deliver high-impact generative AI applications and ensure their reliable operation.

The internship emphasizes hands-on modeling, data prep, and production-ready code, with mentoring and opportunities to learn about LLM optimization, GPU techniques, and scalable architectures.