AI Operations Engineer
Confirmed on the employer's own hiring board on Oct 17, 2026. First seen by Alion on Sep 4, 2026. Cisco Systems scores A on the Alion truth index.
Meet the Team
Join Cisco's Commerce Intelligence Data & Analytics team, a pivotal group delivering seamless intelligence, advanced analytics, and cutting-edge agentic experiences across our business operations. Our mission is to build a scalable, unified, and AI-ready data foundation that drives high-impact business decisions and automated Agentic actions . As a specialized team of engineers and operational experts, we move beyond traditional maintenance to solve complex, large-scale commerce challenges with agentic AI. We achieve this by blending innovation, deep process knowledge, technical expertise, and a relentless focus on business impact, ultimately enhancing operational efficiency and data-driven decision-making at scale.
Your Impact
As an AI Engineer, you will design, build, and operate the autonomous and semi-autonomous agents that power Cisco's commerce operations. You will translate ambiguous operational requirements into reliable multi-step agent workflows - orchestrating LLM reasoning, tool and function calling, retrieval, and integrations into commerce platforms and operational systems. You will be responsible for the full lifecycle: designing the agent and its guardrails, grounding it with retrieval, connecting it to enterprise systems through robust API and agent-to-agent (A2A) integrations, evaluating its behavior, and deploying and monitoring it in production by collaborating with extended team across various time zones. You will partner with cross-functional AI teams to identify high-value opportunities and deliver intelligent, automated workflows that optimize performance and drive durable growth. This role is a pillar to our operational strategy and to Cisco's AI-first vision.
Responsibilities
Architect and build production AI agents - including multi-agent and human-in-the-loop workflows - that automate commerce operations. Work in partnership and guidance from the core team at onsite
Implement agent orchestration with modern frameworks (e.g., LangGraph, LangChain, AutoGen, CrewAI, or equivalent), designing robust tool/function-calling, planning, and state management.
Build agent-to-agent (A2A) integrations and interoperability using emerging standards and protocols (e.g., A2A, MCP), enabling agents to delegate, coordinate, and hand off work reliably.
Design and implement API integrations - REST/GraphQL, event-driven and webhook-based services, authentication, rate limiting, and resilient error handling - to connect agents with commerce platforms and enterprise data sources.
Manage the end-to-end lifecycle of LLM-powered applications using Snowflake Cortex, from model selection and fine-tuning to deployment, versioning, and decommissioning.
Architect robust agentic workflows using Snowflake Native Apps and Stored Procedures, implementing sophisticated tool-calling, planning, and state management that ensures seamless execution across complex operational tasks.
Build and tune retrieval-augmented generation (RAG) pipelines over documents, structured data, and knowledge repositories. Implement and maintain low-latency vector databases, semantic search architectures, and Retrieval-Augmented Generation (RAG) pipelines using native vector data types and search functions.
Build automated monitoring mechanisms to track LLM drift, hallucination rates, and token usage, triggering self-healing alerts and automated model retraining or routing workflows.
Proactively manage and optimize Snowflake credit consumption for AI workloads; implement warehouse auto-scaling, query profiling, and multi-cluster strategies to ensure cost-efficiency at scale.
Design guardrails and human-in-the-loop gates for sensitive actions, and implement LLM routing and failover for reliability and cost control.
Apply prompt engineering and context engineering to make agent behavior accurate, safe, and consistent under real operational conditions.
Own the AI/ML CI/CD pipeline, ensuring rigorous version control, automated testing, and progressive rollout of AI models and agentic workflows across development, staging, and production environments.
A self-starter with proven expertise to deliver outcomes with minimal supervision
Minimum Qualifications
Bachelor’s degree in Computer Science, Data Engineering, AI/ML, or a related technical field, equivalent practical experience.
5+ Years of Advanced proficiency in Python and SQL, with proven experience building, testing, and deploying production-grade data and AI solutions.
Deep technical understanding of the Snowflake Data Cloud especially Cortex AI functions and secure data sharing.
2+ years of Proven experience building and deploying LLM-powered or agentic AI applications in production.
Strong proficiency in Python and hands-on experience with LLM/agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, or the OpenAI/Anthropic SDKs).
Experience with CI/CD practices, version control (Git), and automated testing frameworks in an enterprise environment.
2+ years of Practical experience with RAG, prompt engineering, and tool/function calling.
Demonstrated experience in API integrations and systems integration (REST/GraphQL, event-driven services, authentication, enterprise data sources).
Experience designing or implementing agent-to-agent (A2A) integrations or multi-agent communication and orchestration.
Ability to drive technical delivery and collaborate effectively across cross-functional teams.
Preferred Qualifications
Experience with agent interoperability standards and protocols (e.g., A2A, Model Context Protocol (MCP), OpenAPI-driven tool integration).
Experience designing multi-agent systems, human-in-the-loop workflows, and agent guardrails for enterprise use.
Experience with agent evaluation and observability (eval harnesses, tracing, LLMOps/AgentOps tooling such as LangSmith, Langfuse, or equivalent).
Experience with vector databases and advanced/multimodal retrieval (e.g., hybrid search, reranking, knowledge-graph grounding).
Experience with cloud platforms (Snowflake, AWS, GCP, or Azure) and MLOps/LLMOps practices, including cost-aware LLM routing and FinOps.
Understanding of commerce platforms or supply chain operations.
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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