Software Engineering Technical Leader - AI & Agentic AI

About the role

Cisco's team builds and runs the cloud-native services and AI capabilities that its engineers, customers and the developer community rely on. It takes generative and agentic AI from demos into production, where reliability and security matter. The team is hands-on, with engineers staying close to architects, product and security, and owning their code from first commit through live operations. This Technical Leader role focuses on building and operating secure microservices and integrating AI and agentic capabilities.

Key responsibilities

• Build and operate secure, scalable microservice applications on AWS and Kubernetes, owning reliability and observability
• Develop and integrate AI and agentic capabilities, including LLM integration, retrieval augmented generation, tool and function calling, and agentic workflows
• Apply a security-first approach across development, deployment and operations, covering application, API, container, identity and data security
• Automate testing, delivery and monitoring through modern DevSecOps practice, and troubleshoot complex production issues through to resolution

Required profile

• Bachelor's degree plus 8 years of related experience, or Master's degree plus 6 years, or equivalent related work experience
• 5+ years of professional software engineering experience delivering production applications in Python, Java, Go, JavaScript/TypeScript or C++
• 3+ years developing and operating microservices, REST APIs or distributed applications on a public cloud such as AWS
• 2+ years deploying and operating containerized applications in Kubernetes in production
• 1+ year of hands-on experience building applications with generative AI or large language models, including retrieval augmented generation, embeddings or tool and function calling

Preferred qualifications

• Master's degree in Computer Science, Engineering or Artificial Intelligence and Machine Learning
• Experience developing or integrating AI agents and agentic workflows in an enterprise setting
• Hands-on experience with agent frameworks such as LangGraph, LangChain or Semantic Kernel, and with Model Context Protocol or comparable technologies
• Experience implementing AI security, guardrails, governance, evaluation or observability for AI systems
• Track record of mentoring engineers, contributing to engineering standards and collaborating across teams