Production AI Engineer: LLMs & Data Pipelines

Cisco Systems, Inc; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Job summary

Cisco Systems, Inc. is seeking a Software Engineer to build scalable data pipelines and applied AI systems for networking and security. You will contribute to AI-powered data pipelines, LLM applications, and operational analytics that enhance visibility, anomaly detection, and incident analysis.

You will collaborate with senior engineers to write maintainable code, create user-facing dashboards, and support live production services. Strong Python/SQL skills and cloud tooling are essential.

Qualifications

• Must have strong Python and SQL programming abilities.
• Knowledge of data structures, algorithms, software engineering, and OO design.
• Experience with building production AI applications is a plus.
• Familiarity with cloud-based data platforms and ML tooling.

Responsibilities

• Design and implement scalable ETL/ELT data pipelines using Python/SQL.
• Develop production AI systems (LLMs, RAG, ML) for networking/security use cases.
• Collaborate with senior engineers to deploy dashboards and maintain live services.

Skills

Python

SQL

Data structures

Algorithms

Software engineering

OOP design

Generative AI

Education

Master’s degree in a relevant field

Bachelor’s degree in a relevant field

Bootcamp/Certification with 3 years experience

High school diploma with 4 years experience

Tools

LangChain

Neo4j

AWS

Snowflake

Airflow

PySpark

TensorFlow

OpenCV

React

Job description

Cisco Systems, Inc. is seeking a Software Engineer to build scalable data pipelines and applied AI systems for networking and security. You will contribute to AI-powered data pipelines, LLM applications, and operational analytics that enhance visibility, anomaly detection, and incident analysis.

You will collaborate with senior engineers to write maintainable code, create user-facing dashboards, and support live production services. Strong Python/SQL skills and cloud tooling are essential.