Data Scientist

Job description

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 with privacy, 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 Leverage Generative 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 in Computer Science, Engineering, or equivalent practical experience 3+ years of experience working within or alongside supply chain / enterprise operations environments 2+ years of hands-on experience with agentic AI frameworks (e.g. LangGraph, Google Agent SDK) and MCP server development Experience evaluating AI systems using eval 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 in Object-Oriented Programming (OOP), scalable backend development, and distributed systems Experience building REST APIs and backend services, preferably using Fast API Experience with API integrations, enterprise systems, third-party SDKs, and service orchestration Basic understanding of UX/UI principles to collaborate effectively with design teams and translate user flows into working applications Ability 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 production Experience with Agentic AI systems, autonomous workflows, tool calling, and multi-agent orchestration Strong understanding of MCP (Model Context Protocol), A2A (Agent-to-Agent) communication patterns, and agent integration frameworks Experience building RAG 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 with vector databases and semantic retrieval systems Experience deploying AI applications on AWS / GCP / Azure Experience with Docker, Kubernetes, CI/CD pipelines, and production deployment workflows Ability to design scalable, reliable, and observable AI infrastructure for inference and application workloads Strong development workflow using GitHub, 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.