Senior Manager, AI Engineering

Ralph Lauren Corporation (NYSE:RL) is a global leader in the design, marketing and distribution of premium lifestyle products in five categories: apparel, accessories, home, fragrances, and hospitality. For more than 50 years, Ralph Lauren's reputation and distinctive image have been consistently developed across an expanding number of products, brands and international markets. The Company's brand names, which include Ralph Lauren, Ralph Lauren Collection, Ralph Lauren Purple Label, Polo Ralph Lauren, Double RL, Lauren Ralph Lauren, Polo Ralph Lauren Children, Chaps, among others, constitute one of the world's most widely recognized families of consumer brands.
At Ralph Lauren, we unite and inspire the communities within our company as well as those in which we serve by amplifying voices and perspectives to create a culture of belonging, ensuring inclusion, and fairness for all. We foster a culture of inclusion through: Talent, Education & Communication, Employee Groups and Celebration.
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Purpose & Scope -
The Senior Manager, AI Engineering is responsible for leading the design, development, and deployment of scalable AI solutions that accelerate Ralph Lauren's transformation into an AI-enabled enterprise. This role combines strong technical leadership with hands-on execution to deliver production-grade AI applications, agentic systems, and reusable engineering capabilities across the organization. This leader partners closely with AI Strategy, Enterprise Architecture, Data Engineering, Product, Security, Governance, and Business Technology teams to translate prioritized use cases into secure, scalable, and high-impact AI solutions built on Microsoft Azure and Databricks.
The Role Is Responsible For
Enterprise AI and agentic AI solution delivery
Multi-agent orchestration and intelligent workflow automation
LLMOps and MLOps engineering practices
AI integration, observability, and operational excellence
Responsible AI implementation and governance adherence
Engineering team leadership and capability development
This leader is expected to drive engineering excellence while enabling rapid experimentation, innovation, and scalable AI adoption across business functions.
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Key Roles & Responsibilities
AI Solution Delivery
Lead the design, development, and implementation of enterprise AI solutions aligned with business priorities.
Translate prioritized AI use cases into scalable, production-ready applications and services.
Partner with Product Managers and business stakeholders to define solution requirements, delivery plans, and measurable outcomes.
Drive disciplined execution across prototyping, engineering, testing, deployment, and production support.
Agentic AI & Intelligent Automation
Design and implement agentic AI solutions using Azure AI services, Databricks, vector platforms, orchestration frameworks, and modern LLM ecosystems.
Build and maintain multi-agent workflows, orchestration frameworks, and AI-powered automation solutions.
Develop reusable patterns for memory management, tool usage, reasoning workflows, human-in-the-loop controls, and enterprise action execution.
Evaluate emerging agentic AI technologies based on business value, risk, and engineering fit.
AI Platform Engineering
Contribute to enterprise AI platforms, reusable services, SDKs, templates, and engineering accelerators.
Develop and maintain APIs, integrations, and shared AI capabilities supporting multiple business use cases.
Partner with Data Engineering to ensure AI-ready data foundations and scalable access patterns in Azure and Databricks.
Support technical decisions related to AI services, inference, model deployment, infrastructure, and integrations.
Engineering Excellence & Operations
Establish and promote standards for CI/CD, testing, release management, monitoring, and operational support.
Implement LLMOps and MLOps practices including evaluation, prompt management, experimentation, telemetry, and runtime optimization.
Lead production-readiness reviews for reliability, scalability, security, and cost efficiency.
Monitor performance and drive continuous improvement through operational analytics.
Governance, Security & Responsible AI
Ensure AI solutions comply with enterprise security, privacy, compliance, governance, and Responsible AI standards.
Partner with Security and Governance teams to implement guardrails, auditability, policy enforcement, and risk controls.
Support observability frameworks including telemetry, evaluation, performance tracking, and drift detection.
Team Leadership & Talent Development
Lead, mentor, and develop a team of AI engineers and technical specialists.
Provide technical coaching and hands-on guidance throughout the software development lifecycle.
Foster a culture of innovation, collaboration, engineering rigor, and continuous learning.
Support recruiting, onboarding, performance enablement, and career development.
Drive collaboration across strategy, architecture, data, infrastructure, product, and business teams.
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Qualifications & Experience
Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical discipline.
8+ years of experience in software engineering, AI/ML engineering, or data engineering, including technical leadership responsibility.
3+ years of experience leading engineering teams that deliver AI or machine learning solutions.
Proven experience developing and deploying production-grade AI applications, agentic systems, and intelligent automation solutions.
Strong hands-on engineering skills with the ability to architect, prototype, review, and troubleshoot AI systems.
Deep experience with Microsoft Azure, including Azure AI Services, Azure Machine Learning, AKS, API Management, and cloud-native engineering patterns.
Strong experience with Databricks, Lakehouse architecture, AI/ML pipelines, vector search, and scalable data processing.
Experience building RAG solutions, vector database integrations, memory architectures, and enterprise knowledge-retrieval systems.
Hands-on experience with frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, OpenAI SDKs, MCP-based ecosystems, or equivalent technologies.
Strong understanding of LLMOps and MLOps, including deployment, prompt management, evaluation, observability, experimentation, governance, and runtime optimization.
Experience with API-first architecture, microservices, event-driven systems, cloud-native platforms, and enterprise integration patterns.
Strong communication and stakeholder-management skills across technical and business teams.
Preferred Experience
Experience delivering agentic AI and multi-agent solutions in enterprise environments.
Ability to rapidly prototype, evaluate, and adopt emerging AI technologies and practices.
Experience building reusable engineering frameworks, accelerators, and shared AI services.
Familiarity with AI-native commerce and Agentic Commerce ecosystems, including UCP, ACP, and autonomous transaction orchestration patterns.
Experience in retail, luxury retail, eCommerce, or digital commerce environments.
Relevant Azure, Databricks, cloud engineering, or AI/ML certifications.

Work arrangement
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