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# Software Microservices Platform Engineering Lead/ AI & Agentic AI Architect (Hybrid)

- **Company:** Cisco Systems, Inc.
- **Location:** San Jose, CA, United States
- **Experience:** Expert
- **Salary:** $214,100.0 - $309,800.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Python (Programming Language), Software Engineering, TypeScript, Google Cloud, Large Language Models, Multi-Agent Systems, Generative AI, Containerization, Kubernetes, Information Technology, Free and Open-Source Software, Virtual Agents, Golang, Microservices
- **Published:** September 16, 2026
- **Apply:** https://dejobs.org/x/x/5D9DFC410BF7462486E311B0B22889E6/job/

## About the Role

* Bachelors and 11+ years of related experience, or Masters and 7+ years of related experience, or PhD and 3+ of related experience, or equivalent related work experience.
 * 8+ years of professional software engineering experience delivering production-quality applications using one or more of the following languages: Python, Java, Go, or JavaScript/TypeScript.
 * 5+ years of experience designing, deploying, and operating microservice-based applications on a public cloud platform such as AWS, GCP and Azure.
 * 3+ years of experience running containerized workloads in Kubernetes in a production environment.
 * 2+ years of hands-on experience building applications with generative AI or large language models, including at least one production system using retrieval augmented generation (RAG), embeddings, or tool and function calling., * Master's degree in Computer Science, Engineering, or Artificial Intelligence and Machine Learning.
 * Experience building enterprise-scale agentic AI platforms, including agent orchestration and multi-agent workflows.
 * Hands-on experience with agent frameworks and protocols such as LangGraph, LangChain, Model Context Protocol, or Semantic Kernel.
 * Experience with AI security, guardrails, policy enforcement, evaluation, and observability for AI systems in production.
 * Ability to mentor engineers and influence architecture decisions across organizational boundaries without direct authority, evidenced through patents, publications, open-source contributions, or major engineering initiatives.



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