AI Engineering Lead

Qualifications

EXPERIENCE, SKILLS & KNOWLEDGE Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, Data Engineering, Data Science, or related technical discipline. Significant experience in enterprise data engineering, cloud data platforms, distributed systems, and large-scale data architecture. Proven experience building and operationalizing enterprise data platforms supporting AI, analytics, and digital transformation initiatives.

Deep expertise in Microsoft Azure data ecosystem including Azure Data Factory, Azure Synapse, Azure Storage, Event Hub, API integrations, and cloud-native engineering patterns. Strong hands-on experience with Databricks including Lakehouse architecture, Delta Lake, scalable ETL/ELT pipelines, streaming frameworks, and AI-ready data engineering workflows. Strong understanding of enterprise data architecture, semantic modeling, metadata management, interoperability, and master data management principles.

Experience designing scalable ingestion, orchestration, transformation, and reusable data product frameworks across structured and unstructured data domains. Strong expertise in enterprise data governance including quality management, lineage, cataloging, stewardship, retention, and compliance frameworks. Experience enabling AI and machine learning use cases through scalable and governed data foundations. Understanding of AI-oriented data architectures including vector-ready pipelines, RAG enablement, embeddings lifecycle management, and unstructured data processing.

Strong understanding of API-first architectures, event-driven systems, distributed processing, and cloud-native platform engineering. Experience implementing secure, scalable, and governed enterprise data platforms within complex enterprise environments. Demonstrated ability to proactively identify and mitigate data risks before they become delivery blockers. Strong operational mindset with focus on platform reliability, scalability, observability, and engineering excellence.

Ability to balance detailed governance requirements with enterprise-scale strategy and business agility. Strong leadership and mentoring capabilities with experience building high-performing engineering organizations and establishing engineering standards. Excellent communication and stakeholder management skills with the ability to influence technical teams, business stakeholders, and executive leadership.

Preferred Experience

Experience supporting enterprise AI and agentic AI initiatives through scalable AI-ready data ecosystems. Familiarity with AI/ML data pipelines, feature stores, vector databases, knowledge graph architectures, and retrieval systems. Experience partnering across AI Engineering, D&A, Governance, Enterprise Architecture, and Product organizations within a Center of Excellence operating model. Experience supporting retail, luxury retail, eCommerce, or digital commerce environments with large-scale customer, product, inventory, and transactional data ecosystems.

Familiarity with DataOps, MLOps, AI governance, and enterprise observability tooling. Experience modernizing legacy enterprise data platforms into cloud-native Lakehouse or composable data architectures. Exposure to real-time streaming, operational analytics, and event-driven platform ecosystems. Experience operating within global, multi-region enterprise environments with complex governance and compliance requirements.

Ralph Lauren will consider for employment qualified applicants with arrest or conviction records in a manner consistent with the requirements of the law, including any applicable fair chance laws. Please note background checks will be evaluated individually.