Senior AI Platform Architect & Agentic Microservices Lead

Cisco Systems, Inc.; San Jose (CA); Hybrid; USD 214,000 - 310,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Parental leave and disability coverage

Job summary

Cisco Systems, Inc. seeks a senior software engineer to architect and implement AI and agentic AI platforms, focusing on scalable microservices on AWS and Kubernetes. You will drive security-first design, establish reusable patterns, and mentor engineers to raise overall delivery quality.

Lead design reviews and contribute to production systems with guardrails, reliability metrics, and cross-team collaboration to accelerate AI adoption across the organization.

Qualifications

• 8+ years of professional software engineering delivering production-quality applications.
• 5+ years designing, deploying, and operating microservice-based apps on AWS, GCP, or Azure.
• 3+ years running containerized workloads in Kubernetes in production.
• 2+ years building applications with generative AI or LLMs, incl. retrieval-augmented generation.

Responsibilities

• Architect and build AI and agentic AI solutions, including agent orchestration and tool integration.
• Design and operate secure microservice applications on AWS and Kubernetes with a security-first approach.
• Establish architectural patterns and engineering practices adopted by other teams.
• Lead design and code reviews and mentor engineers to scale technical judgment.
• Deliver agentic systems with measurable reliability and guardrails in production.

Skills

Python

Java

Go

JavaScript/TypeScript

Education

Bachelor's degree in CS/Engineering

Master's degree preferred

PhD helpful

Tools

AWS

Kubernetes

Job description

Cisco Systems, Inc. seeks a senior software engineer to architect and implement AI and agentic AI platforms, focusing on scalable microservices on AWS and Kubernetes. You will drive security-first design, establish reusable patterns, and mentor engineers to raise overall delivery quality.

Lead design reviews and contribute to production systems with guardrails, reliability metrics, and cross-team collaboration to accelerate AI adoption across the organization.