AI Operations Engineer

Job description

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&#43; 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&#43; 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&#43; 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). • <spa