Meet the Team
We are the Supply Chain Transformation AI Team within Cisco’s Supply Chain Operations. We are a diverse, fast-moving group of AI engineers and data scientists who collaborate directly with Product Operations. We don’t 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 automation
- Analyze and improve business processes by identifying bottlenecks, inefficiencies, and areas where AI-driven workflows can deliver measurable business value
- Design and deliver end-to-end AI solutions that integrate with enterprise systems, data sources, and existing user workflows in an intuitive and scalable way
- Build, pilot, and productionize AI applications, working closely with end-users to gather feedback, iterate quickly, and drive adoption across teams
- Collaborate cross-functionally with engineering, product, design, and business stakeholders to translate ambiguous problems into practical AI solutions
- Ensure solutions align withprivacy, security, compliance, and responsible AI practices, especially when handling enterprise or sensitive data
- Create reusable frameworks, components, and documentation to accelerate future AI development and improve consistency across teams
- LeverageGenerative AI and automation to accelerate prototyping, research, documentation, workflow execution, and operational efficiency across day-to-day processes
Minimum Qualifications
- Bachelor’s or Master’s degree inComputer Science, Engineering, or equivalent practical experience
- 3+ years of experience working within or alongsidesupply chain / enterprise operations environments
- 2+ years of hands-on experience withagentic AI frameworks (e.g. LangGraph, Google Agent SDK) andMCP server development
- Experience evaluating AI systems usingeval frameworks, testing pipelines, or human-in-the-loop review workflows
- Strong 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 inObject-Oriented Programming (OOP), scalable backend development, and distributed systems
- Experience buildingREST APIs and backend services, preferably usingFast API
- Experience with API integrations, enterprise systems, third-party SDKs, and service orchestration
- Basic understanding ofUX/UI principles to collaborate effectively with design teams and translate user flows into working applications
- Ability to rapidly prototype and ship features usingAI-assisted coding tools (e.g. GitHub Copilot, Cursor, Claude Code, etc.)
- Hands-on experience building and deployingLLM-powered applications in production
- Experience withAgentic AI systems, autonomous workflows, tool calling, and multi-agent orchestration
- Strong understanding ofMCP (Model Context Protocol),A2A (Agent-to-Agent) communication patterns, and agent integration frameworks
- Experience buildingRAG pipelines including embeddings, retrieval strategies, reranking, context management, and evaluation
- Strong prompt engineering skills including prompt design, structured outputs, guardrails, and workflow optimization
- Experience working withvector databases and semantic retrieval systems
- Experience deploying AI applications onAWS / GCP / Azure
- Experience withDocker,Kubernetes, CI/CD pipelines, and production deployment workflows
- Ability to design scalable, reliable, and observableAI infrastructure for inference and application workloads
- Strong development workflow usingGitHub,GitHub Actions, and modern AI-native engineering practices
- Experience 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, 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.
