Interested in this AI/ML Engineer role at Ralph Lauren?
Apply Now →Skills & Technologies
About This Role
Ref#: W182956
Department: Information Technology
City: Nutley
State/Province: New Jersey
Location: United States
Pay Range: $130,900 - $266,865 annually; actual pay is dependent on experience and geographic location
Company DescriptionRalph 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.
Position OverviewThis Leader is responsible for defining and operationalizing the enterprise data architecture, governance, and platform strategy that enables scalable AI, analytics, and digital initiatives across the organization. Reporting to the Head of AI & Data Engineering, this role serves as the senior data engineering leader within the AI Center of Excellence (AI CoE), ensuring that enterprise data assets are trusted, governed, reusable, and AI-ready.
This leader partners closely with the AI Engineering, Data CoE, Enterprise Architecture, Security, Governance, and Product organizations to establish the foundational data capabilities required to scale enterprise AI adoption. The role ensures that AI and analytics use cases are supported by scalable, secure, interoperable, and high-quality data platforms before engineering investment and solution delivery begins.
The Leader is accountable for:
- Enterprise data architecture supporting AI and analytics
- AI-ready data platform engineering and modernization
- Data governance, lineage, quality, cataloging, and stewardship
- Scalable ingestion, transformation, and reusable data product frameworks
- Cross-CoE platform alignment and operational governance
- Data readiness and onboarding for AI use cases
- Enterprise data engineering standards and operational excellence
This role acts as the primary accountability leader for ensuring data quality and readiness are treated as foundational requirements for enterprise AI success.
Essential Duties & ResponsibilitiesEnterprise Data Architecture & Platform Strategy
- Define and operationalize enterprise data architecture standards supporting AI, analytics, digital commerce, and enterprise platform modernization initiatives.
- Establish scalable, cloud-native data platform strategies leveraging Microsoft Azure, Databricks, Lakehouse architectures, and modern distributed data engineering frameworks.
- Drive the evolution of enterprise data ecosystems to support AI-ready, real-time, interoperable, and reusable data services.
- Define reusable engineering patterns for ingestion, transformation, orchestration, semantic modeling, metadata management, and data product delivery.
Data Readiness & AI Enablement
- Serve as the enterprise data accountability leader for AI use case onboarding and delivery readiness.
- Lead the creation and management of enterprise data products, ensuring trusted, reusable, and scalable data assets that power analytics, AI, and intelligent applications.
- Translate business challenges into data, analytics, and AI opportunities, partnering across functions to define roadmaps, success metrics, and value realization plans.
- Partner closely with AI Engineering to enable the development and deployment of AI solutions, agents, copilots, and intelligent workflows through well-governed data products and platforms.
- Validate data availability, quality, lineage, governance, scalability, and operational readiness prior to AI engineering investment and solution development.
- Partner closely with the AI Engineering Lead to enable scalable AI pipelines, vector-ready architectures, retrieval systems, and AI-driven data services.
- Ensure enterprise AI systems have access to secure, governed, reusable, and high-quality data assets.
Data Governance, Quality & Compliance
- Align with the D&A CoE to establish governance standards for AI-related data products and enterprise data assets across all regions.
- Define enterprise standards for data quality, lineage, metadata, cataloging, stewardship, retention, and lifecycle management.
- Partner with governance, security, privacy, and compliance teams to ensure data platforms align with enterprise policies and regulatory requirements.
- Establish monitoring and operational controls for data quality, observability, platform reliability, and pipeline performance.
Data Engineering Delivery & Operational Excellence
- Lead enterprise data platform initiatives that directly enable the AI roadmap and analytics strategy.
- Oversee scalable ingestion pipelines, streaming architectures, transformation frameworks, and reusable data engineering services.
- Drive engineering best practices across CI/CD, DataOps, testing automation, observability, release management, and operational support.
- Ensure enterprise data platforms are scalable, resilient, secure, performant, and operationally sustainable.
Cross-COE Alignment & Stakeholder Leadership
- Drive alignment between AI CoE and D&A CoE on data standards, platform strategy, engineering ownership, and operational handoffs.
- Navigate organizational boundaries effectively while minimizing duplication, delivery friction, and platform fragmentation.
- Partner with enterprise architecture and infrastructure teams to align platform modernization and integration strategies.
- Serve as a trusted advisor to business and technology leadership on enterprise data strategy, platform investments, and AI enablement priorities.
Organizational Leadership
- Build and mentor high-performing data engineering teams across platform engineering, pipeline engineering, data operations, and governance disciplines.
- Foster a culture centered on data quality, operational rigor, scalability, governance, and engineering excellence.
- Establish reusable delivery models, engineering standards, and operational governance processes.
- Develop long-term capability roadmaps aligned to enterprise AI, analytics, and digital transformation objectives.
Experience, Skills, and KnowledgeQualifications
- 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.
Salary Context
This $130K-$266K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Ralph Lauren, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($198K) sits 6% below the category median. Disclosed range: $130K to $266K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Ralph Lauren AI Hiring
Ralph Lauren has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span New York, NY, US, Nutley, NJ, US. Compensation range: $266K - $370K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.