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Meet the Team We are the Supply Chain Transformation AI Team within Ciscos Supply Chain Operations. We are a diverse, fast-moving group of AI engineers and data scientists who collaborate directly with Product Operations. We dont just analyze data; we transform it into actionable intelligence. By building advanced AI solutions, we empower our NPI (New Product Introduction) PMs, Product, and Test Engineering teams to anticipate market shifts, optimize workflows, and meet the evolving demands of our product lifecycle. Your Impact We are looking for a highly skilled AI Engineer with a strong foundation in software engineering and proven experience in building and deploying modern AI solutions. This role will focus on developing scalable, enterprise-grade applications powered by Generative AI, LLMs, AI agents, and RAG architectures, while ensuring robust engineering practices across development, deployment, and production support. Core Responsibilities Partner with business and functional teams to understand workflows, pain points, and operational goals, and identify high-impact opportunities for AI and automationAnalyze and improve business processes by identifying bottlenecks, inefficiencies, and areas where AI-driven workflows can deliver measurable business valueDesign and deliver end-to-end AI solutions that integrate with enterprise systems, data sources, and existing user workflows in an intuitive and scalable wayBuild, pilot, and productionize AI applications, working closely with end-users to gather feedback, iterate quickly, and drive adoption across teamsCollaborate cross-functionally with engineering, product, design, and business stakeholders to translate ambiguous problems into practical AI solutionsEnsure solutions align with privacy, security, compliance, and responsible AI practices, especially when handling enterprise or sensitive dataCreate reusable frameworks, components, and documentation to accelerate future AI development and improve consistency across teamsLeverage Generative AI and automation to accelerate prototyping, research, documentation, workflow execution, and operational efficiency across day-to-day processes Minimum Qualifications Bachelors or Masters degree in Computer Science, Engineering, or equivalent practical experience3+ years of experience working within or alongside supply chain / enterprise operations environments2+ years of hands-on experience with agentic AI frameworks (e.g. LangGraph, Google Agent SDK) and MCP server developmentExperience evaluating AI systems using eval frameworks, testing pipelines, or human-in-the-loop review workflowsStrong problem-solving skills, attention to detail, self-driven, and ability to manage multiple priorities in a fast-paced environment. Technical Skills Strong computer science fundamentals with hands-on experience in Object-Oriented Programming (OOP), scalable backend development, and distributed systemsExperience building REST APIs and backend services, preferably using Fast APIExperience with API integrations, enterprise systems, third-party SDKs, and service orchestrationBasic understanding of UX/UI principles to collaborate effectively with design teams and translate user flows into working applicationsAbility to rapidly prototype and ship features using AI-assisted coding tools (e.g. GitHub Copilot, Cursor, Claude Code, etc.)Hands-on experience building and deploying LLM-powered applications in productionExperience with Agentic AI systems, autonomous workflows, tool calling, and multi-agent orchestrationStrong understanding of MCP (Model Context Protocol), A2A (Agent-to-Agent) communication patterns, and agent integration frameworksExperience building RAG pipelines including embeddings, retrieval strategies, reranking, context management, and evaluationStrong prompt engineering skills including prompt design, structured outputs, guardrails, and workflow optimizationExperience working with vector databases and semantic retrieval systemsExperience deploying AI applications on AWS / GCP / AzureExperience with Docker, Kubernetes, CI/CD pipelines, and production deployment workflowsAbility to design scalable, reliable, and observable AI infrastructure for inference and application workloadsStrong development workflow using GitHub, GitHub Actions, and modern AI-native engineering practicesExperience owning the full lifecycle of AI applications: architecture development deployment production support Preferred Qualifications Track record of thriving in fast-paced, evolving environments where you must define the path forward despite incomplete information or shifting priorities.Experience with MLOps practices (MLflow, Kubeflow, or similar) to manage the model lifecycle.Experience working with large-scale, unstructured datasets and multi-modal data. Why Cisco At Cisco, were revolutionizing how data and infrastructure connect and protect organizations in the AI era and beyond. Weve Meet