AI Operations Engineer

The application window is expected to close on: 10/08/2026 2021485 – AI Operations Engineer (Hybrid) This position is listed as hybrid – eligible candidates should expect to be present in the Raleigh-Cisco office on a semi-regular basis. Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received. Meet the Team Join Cisco’s Commerce Operations Engineering Center (COEC) as an AI Engineer, where you will be part of a dynamic team driving innovation through AI-powered automation. Collaborate with multi-functional AI, engineering, and operations teams to transform sophisticated operational challenges into reliable, scalable AI solutions that advance Cisco’s AI-first future.

As an AI Engineer in COEC, you will design, build, and operate production-ready AI agents that power Cisco’s Commerce Operations. You will translate complex operational needs into automated workflows by orchestrating LLM reasoning, tool/function calling, retrieval, enterprise integrations, and agent-to-agent communication. You will be responsible for the full lifecycle of AI agent delivery—from solution design, guardrails, and system integration to evaluation, deployment, monitoring, and continuous improvement. Your work will help eliminate manual tasks, improve speed and accuracy, and drive operational excellence. What You’ll Do Design and build AI agents that automate manual and repetitive Commerce Operations tasks. Implement agent orchestration using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or equivalent. Build agent-to-agent integrations using emerging standards such as A2A and MCP to enable reliable coordination and delegation. Design secure API integrations with commerce platforms and enterprise data sources using REST, GraphQL, webhooks, event-driven services, authentication, rate limiting, and error handling. Build and optimize retrieval-augmented generation (RAG) pipelines using documents, structured data, and knowledge repositories. Implement guardrails, human approval gates, LLM routing, and failover mechanisms to ensure reliability, safety, and cost control. Apply prompt and context engineering to ensure AI agents deliver accurate, safe, and consistent outcomes. Build evaluation and observability capabilities, including offline test sets, online monitoring, tracing, audit logging, and quality regression detection. Own deployment and production operations, including CI/CD, progressive rollout, rollback, monitoring, model/version control, and cost tracking. Supervise AI solution performance and report business impact through critical metrics such as efficiency gains, cycle-time reduction, incident reduction, and operational accuracy. Continuously improve AI workflows, tools, and methodologies to increase reliability, scalability, and business value. Partner with Commerce Operations, application development, infrastructure, and business teams to integrate AI solutions into existing systems and workflows.

7+ years of experience in software engineering or a related field within an enterprise environment. Shown experience building and deploying LLM-powered or agentic AI applications in production. Strong Python skills and tactical experience with LLM/agent frameworks such as LangChain, LangGraph, AutoGen, CrewAI, OpenAI SDK, Anthropic SDK, or equivalents. Practical expertise in retrieval-augmented generation (RAG), prompt engineering, tool/function calling, agent orchestration, and multi-agent communication or workflow orchestration. Experience with API and system integrations, including REST, GraphQL, event-driven services, authentication, and enterprise data sources. Demonstrated ability to lead technical delivery and collaborate effectively with multi-functional business and engineering teams.

Experience with agent interoperability standards and protocols, including A2A, Model Context Protocol (MCP),