Sr. Architect, AI & Automation

Foot Locker•,

1d•$150,000 - $190,000•Remote

About The Position

Foot Locker is seeking an experienced and innovative Sr. Architect, AI & Automation to lead the enterprise architecture vision for Generative AI, Artificial Intelligence, Intelligent Automation, and AI-powered digital transformation. The role designs scalable AI platforms, reusable frameworks, reference architectures, and enterprise standards that enable adoption across Merchandising, Supply Chain, Stores, Digital, Finance, and Customer domains. The architect partners with business and technology leaders on next-generation solutions using LLMs, AI agents, RAG, intelligent automation, and cloud-native platforms; establishes enterprise AI governance; and helps transform business processes into AI-enabled products and experiences. The team’s AI products are used by store, merchandising, supply chain, digital commerce, and corporate teams across Foot Locker’s brands and digital channels. The role sets enterprise patterns, guardrails, and build-versus-buy decisions across teams, with accountability for reusability and governance rather than a single product. This position is 100% remote and based in the United States. Foot Locker will not sponsor new employment authorization applicants or provide immigration-related support for this position. The role has no direct reports but provides architectural direction across multiple engineering teams and mentors senior engineers, architects, and contract resources. Candidates should be able to communicate clearly, work collaboratively, exercise sound judgment, plan and execute complex work, and solve problems in situations with limited standardization. A bachelor’s degree or an associate degree in programming, together with the stated related experience or an equivalent combination of education and experience, is specified in the education and experience section.

Requirements

At least 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 or 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.
Bachelor’s degree from a four-year college or university, or an associate degree in programming from a technical college, and 9 or more years of related experience and/or training, or an equivalent combination of education and experience.
Ability to communicate clearly, interpret written and other forms of communication, prepare management reports and professional correspondence, and speak effectively with peers, management, customers, and associates.
Ability to plan and execute unusual and difficult work, take initiative, make decisions when appropriate, solve practical and impractical problems, interpret varied instructions, and deal constructively with conflict and disagreement.
Ability to work well within a team atmosphere and adapt to helping with a variety of department goals.

Nice To Haves

Experience in Retail, Digital Commerce, Supply Chain, or Customer Analytics domains.
Experience working in Agile product delivery environments.
Azure AI Engineer, Azure Solutions Architect, or equivalent certifications.
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.
Retail, supply chain, merchandising, inventory, customer analytics, or other large-scale enterprise domain experience.
Public cloud experience, preferably Azure or AWS.
Previous experience at a Fortune 500 company.

Responsibilities

Define the enterprise AI, GenAI, Agentic AI, and Automation architecture and roadmap in alignment with business and technology strategy.
Architect scalable solutions using LLMs, RAG, AI agents, workflow automation, APIs, and event-driven architectures.
Define architecture and integration for agents, skills, workflow orchestration, MCP services, development tools, and reusable libraries.
Establish patterns for AI agents and multi-agent workflows, including orchestration, graph and state design, conditional routing, tool and API calling, memory, checkpointing, guardrails, human-in-the-loop processes, and observability.
Design enterprise RAG architectures that integrate structured and unstructured data, vector databases, embeddings, semantic search, ontology and knowledge graph models, and enterprise knowledge repositories.
Integrate AI capabilities with enterprise data platforms such as Snowflake, Databricks, Azure, and Power BI, and with operational applications.
Define LLMOps best practices for lifecycle management, prompt and version management, testing, evaluation, deployment, monitoring, observability, and cost optimization, including measures for groundedness, retrieval quality, task success, latency, and cost per task.
Partner with business and technology leaders to translate strategic goals into scalable AI and automation solutions.
Define standards for Responsible AI, security, privacy, RBAC, PII protection, data governance, model governance, and regulatory compliance.
Evaluate AI technologies, foundation models, frameworks, and vendors and provide architecture recommendations based on business requirements.
Design reusable AI services, APIs, connectors, accelerators, and reference architectures to support adoption across teams.
Provide technical leadership and mentor architects, data engineers, software engineers, and analytics teams.