Senior Solution Architect / Delivery Manager

What matters most

• AI-Powered Supply Chain Transformation

  The primary objective is to lead the end-to-end design and delivery of AI and LLM-powered applications specifically for demand/supply planning, manufacturing, and inventory management. Success requires translating complex supply chain domain challenges into actionable AI roadmaps that demonstrate clear ROI.

• Agentic AI and Workflow Architecture

  You must architect and implement advanced AI systems, including multi-agent orchestration, RAG, and autonomous workflows. This involves moving beyond simple models to build scalable, production-ready agentic systems that integrate with enterprise data platforms like Snowflake and Oracle ERP.

• AI-Ready Data Governance

  Defining and driving data quality, governance, and accessibility standards is critical. You are expected to ensure that the underlying data infrastructure supports AI initiatives while maintaining enterprise standards for security, ethics, and bias mitigation.

• Cross-Functional Stakeholder Leadership

  The role requires bridging the gap between technical teams (engineering, data science) and business units (supply chain, finance). You must lead large-scale programs from strategy through deployment, ensuring adoption and continuous optimization through effective change management.

Likely challenges to think through

• Balancing Innovation with Enterprise Constraints

  Implementing cutting-edge technologies like multi-agent systems within a large, established manufacturing environment often creates friction with legacy systems (e.g., Oracle ERP). Prepare to discuss how you manage the trade-off between rapid AI experimentation and the need for stable, secure, and reproducible enterprise-grade deployments.

• Quantifying ROI for Emerging AI

  Stakeholders may be skeptical of the business value of experimental AI projects. You will face the challenge of building business cases that translate abstract AI capabilities into measurable supply chain improvements. Prepare to demonstrate your methodology for defining KPIs and cost-benefit analysis for AI initiatives.

• Managing Data Quality for AI Readiness

  Supply chain data is often siloed or inconsistent. You will likely encounter significant hurdles in preparing data for RAG or fine-tuning. Prepare to discuss your approach to data governance and how you enforce quality standards across diverse, global cross-functional teams.

Topics to prepare

• Agentic AI and Multi-Agent Orchestration

  The role requires designing workflows where autonomous agents interact to solve supply chain challenges. Exercise: Review architectural patterns for multi-agent systems, focusing on how agents handle task decomposition, tool calling, and state management. This is directly relevant to the requirement for implementing agentic workflows in production.

• AI-Ready Data Governance and Strategy

  You must define standards for data quality and accessibility to support AI models. Exercise: Outline a framework for ensuring data lineage, bias mitigation, and security in a large-scale enterprise environment. This aligns with the responsibility to shape Cisco's AI-ready data strategy.

• Supply Chain Domain Integration

  The role bridges technical AI solutions with manufacturing and inventory management. Exercise: Map specific AI use cases (e.g., predictive maintenance or demand forecasting) to standard supply chain KPIs like IBP or inventory turnover. This is essential for building business cases with clear ROI.

Skills to revise

• RAG and Semantic Retrieval

  Hands-on implementation of Retrieval-Augmented Generation is a core requirement. Exercise: Design a RAG pipeline architecture that includes vector database selection, embedding strategies, and reranking mechanisms. This is critical for the stated need to deploy LLM-powered applications.

• AI Evaluation Frameworks

  The role requires ensuring model performance and reliability. Exercise: Research and document an evaluation strategy for LLM outputs, including metrics for accuracy, hallucination detection, and guardrail implementation. This addresses the preferred qualification for AI evaluation expertise.

• Prompt Engineering for Structured Outputs

  You must ensure AI systems produce reliable, machine-readable data. Exercise: Practice creating system prompts that enforce strict JSON or schema-based outputs and implement guardrails to handle edge cases. This is vital for integrating AI with enterprise systems like Oracle ERP.

• Map Supply Chain Domain Expertise to AI Solutions

  Prepare a structured walkthrough of a past project where you applied data science or AI to supply chain challenges like demand planning, inventory management, or manufacturing operations. Clearly articulate the business problem, the technical architecture used, and the measurable ROI achieved.

• Develop an Architectural Framework for Agentic AI

  Create a high-level design document for an AI-powered supply chain application. Include your approach to multi-agent orchestration, RAG pipelines, and tool calling. Be ready to discuss how you ensure model governance, ethics, and bias mitigation within this architecture.

• Prepare Evidence of Large-Scale Program Delivery

  Draft a summary of your experience leading cross-functional teams through the full lifecycle of a digital transformation program. Focus on your ability to translate complex technical requirements into actionable roadmaps and your experience managing stakeholders across IT, engineering, and business units.

• Formulate Strategic Questions for the Hiring Team

  Prepare thoughtful questions regarding Cisco's current AI-ready data strategy, such as how they approach data quality and governance for LLM fine-tuning, or how they prioritize supply chain use cases for multi-agent system deployment.

• Review Technical Integration and Platform Knowledge

  Refresh your knowledge on integrating AI solutions with enterprise systems like Oracle ERP and MES. Be prepared to discuss how you would design a modern data platform using tools like Snowflake to support scalable, AI-driven supply chain operations.