AI Operations Engineer

Preferred Qualifications

• Experience with agent interoperability standards and protocols, including A2A, Model Context Protocol (MCP), and OpenAPI-based tool integration.
• Skilled in designing enterprise-grade multi-agent systems, human-in-the-loop workflows, and implementing agent guardrails.
• Validated experience with agent evaluation and observability tools such as eval harnesses, tracing, LangSmith, Langfuse, or equivalent AgentOps/LLMOps platforms.
• Knowledge of vector databases and sophisticated retrieval methods, including hybrid search, reranking, multimodal retrieval, and knowledge-graph grounding.
• Familiarity with AI governance and safety practices, including RBAC, audit logging, PII handling, and controls for high-risk automated actions.
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps/LLMOps practices, including cost-aware LLM routing and FinOps.
• Understanding of commerce platforms, commerce operations, or supply chain operations.
• Consistent record of leading end-to-end AI or agentic AI delivery in sophisticated enterprise environments.
• Advanced degree in AI, Machine Learning, Computer Science, or related field preferred.

Why Cisco?

Qualifications

• 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), and OpenAPI-based tool integration.
• Skilled in designing enterprise-grade multi-agent systems, human-in-the-loop workflows, and implementing agent guardrails.
• Validated experience with agent evaluation and observability tools such as eval harnesses, tracing, LangSmith, Langfuse, or equivalent AgentOps/LLMOps platforms.
• Knowledge of vector databases and sophisticated retrieval methods, including hybrid search, reranking, multimodal retrieval, and knowledge-graph grounding.
• Familiarity with AI governance and safety practices, including RBAC, audit logging, PII handling, and controls for high-risk automated actions.
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps/LLMOps practices, including cost-aware LLM routing and FinOps.
• Understanding of commerce platforms, commerce operations, or supply chain operations.
• Consistent record of leading end-to-end AI or agentic AI delivery in sophisticated enterprise environments.
• Advanced degree in AI, Machine Learning, Computer Science, or related field preferred.
• Why Cisco?
• At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.
• Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere.
• We are Cisco, and our power starts with you.
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Responsibilities

• 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.