Sr. Architect, AI & Automation

Foot Locker•UNAVAILABLE, UNAVAILABLE

2d•Remote

About The Position

Foot Locker is seeking an experienced Sr. Architect, AI & Automation to define and lead enterprise architecture for Generative AI, Artificial Intelligence, intelligent automation, and AI-powered digital transformation. The role establishes scalable platforms, reusable frameworks, reference architectures, and enterprise standards to support adoption across Merchandising, Supply Chain, Stores, Digital, Finance, and Customer domains. AI products supported by the team are used by store, merchandising, supply chain, digital commerce, and corporate teams across Foot Locker brands and digital channels. The position partners with business and technology leaders to develop next-generation AI solutions using LLMs, AI agents, RAG, intelligent automation, and cloud-native platforms. It sets enterprise patterns, guardrails, and build-versus-buy decisions across teams, with accountability for reusability and governance rather than any single product. The role has no direct reports but provides architectural direction across multiple engineering teams and mentors senior engineers, architects, and contract resources. This role is 100% remote and US-based. Foot Locker will not sponsor new employment authorization or provide immigration-related support for this position. Candidates are asked not to use AI tools during interviews or assessments; cameras must be on for virtual interviews.

Requirements

At least 7 years of experience in software engineering, data engineering, ML/AI engineering, automation, or related areas, including hands-on delivery of 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.
Strong communication and presentation skills and 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 work effectively in a team atmosphere, demonstrate motivation and attention to detail, and help with a variety of department goals.
Ability to plan and execute unusual and difficult work, exercise initiative and sound judgment, solve problems with limited standardization, and interpret written, oral, diagram, or schedule-form instructions.
Ability to communicate clearly, read and interpret communications, write reports and professional correspondence, and speak effectively before peers, management, customers, and other associates.
Must be able to sit and talk or hear regularly, lift and/or move up to 10 pounds regularly, and meet close-vision and focus-adjustment requirements, with reasonable accommodations as applicable.

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, 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 aligned 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 integrating 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, as well as 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 enable adoption across multiple teams.
Provide technical leadership and mentorship to architects, data engineers, software engineers, analytics teams, senior engineers, and contract resources.
Translate complex AI technologies into business capabilities, architecture roadmaps, and measurable outcomes.
Plan and execute difficult work, take initiative, solve practical and nonstandard problems, interpret instructions, and deal constructively with conflict and disagreement.
Communicate clearly, interpret communications, write management reports and professional correspondence, and speak effectively with peers, management, customers, and other associates.