7+ years of experience in software engineering, data engineering, ML/AI engineering, automation, or related areas, with hands-on experience delivering AI solutions Strong hands-on understanding of Generative AI, LLMs, Agentic AI, RAG, embeddings, vector search, prompt engineering, and AI orchestration Experience with AI frameworks/platforms such as Azure OpenAI/OpenAI APIs, LangChain, LangGraph, Semantic Kernel, or equivalent technologies Strong cloud architecture experience, preferably Microsoft Azure, including identity, networking, security, APIs, containers, serverless services, and integration patterns Strong experience with modern data platforms such as Snowflake and/or Databricks Knowledge of Python, SQL, REST APIs, JSON, microservices, event-driven architecture, and integration patterns Experience with LLMOps, CI/CD, GitHub/Azure DevOps, infrastructure-as-code, automated testing, monitoring, and observability Strong understanding of enterprise data governance, AI governance, security, privacy, RBAC, and responsible AI Experience integrating AI solutions with enterprise applications, data platforms, BI platforms, APIs, and workflow systems Ability to translate complex AI technologies into business capabilities, architecture roadmaps, and measurable outcomes Strong communication and presentation skills with the ability to influence engineering teams, business stakeholders, directors, and executive leadership Experience in Retail, Digital Commerce, Supply Chain, or Customer Analytics domains preferred Experience working in Agile product delivery environments preferred Azure AI Engineer, Azure Solutions Architect, or equivalent certifications preferred PREFERRED SKILLS Experience with Snowflake Cortex AI, Databricks, the Microsoft Copilot ecosystem, vector databases, AI gateways, Model Context Protocol (MCP), agent-to-agent integration, and enterprise knowledge management, or equivalent technologies would be valuable. Partner closely with business and technology leaders to architect next-generation AI solutions leveraging Large Language Models (LLMs), AI Agents, Retrieval Augmented Generation (RAG), intelligent automation, and modern cloud-native platforms.