Ralph Lauren ML-Ops Manager

Ralph Lauren; India; On-site; INR 4,500,000 - 6,500,000; Full time

Job summary

Ralph Lauren is seeking an ML Ops Engineering Manager to lead the delivery and operational excellence of the company’s ML Ops capabilities. You will manage a team of ML Ops engineers and partner with Data Science, Data Engineering, Platform, and Governance teams to deploy and operate models securely and efficiently across Ralph Lauren's AI portfolio.

The role emphasizes execution, engineering rigor, team leadership, and cross-functional coordination, while model strategy remains with Data

Qualifications

• 7-10 years of experience in MLOps, ML engineering, DevOps, or platform engineering with leadership.
• Hands-on CI/CD, containerization, and orchestration for ML workloads.
• Experience operating model registries, monitoring tooling, and ML pipelines in enterprise environments.
• Working knowledge of cloud platforms (Azure preferred) and Databricks-based ecosystems.
• Strong stakeholder management across data science, engineering, platform, and governance teams.

Responsibilities

• Lead end-to-end delivery of CI/CD pipelines, model registry practices, and deployment infrastructure for ML and AI use cases.
• Drive predictable execution of the ML Ops roadmap in partnership with Data Science and Platform leadership.
• Establish and enforce engineering standards for model packaging, testing, deployment, and rollback.
• Proactively manage delivery risks, technical dependencies, and production incidents across deployed models.
• Ensure ML Ops solutions align with enterprise data and AI platform standards and architecture patterns.
• Partner with platform and architecture teams to design scalable, cost-effective serving and training infrastructure.
• Guide teams on appropriate use of shared compute, environments, and model infrastructure.
• Embed model monitoring, drift detection, and performance alerting into pipelines as standard practice.
• Ensure model versioning, lineage, and documentation requirements are met to support auditability.
• Partner with data governance, security, and compliance teams to ensure responsible and compliant AI deployment.
• Drive CI/CD maturity for ML pipelines, including automated testing, staged rollouts, and controlled promotions.
• Ensure deployed models are operationally ready with monitoring, alerting, and clear incident ownership.
• Continuously improve reliability, latency, and cost-efficiency of training and inference workloads.
• Partner with Data Science and AI leadership to translate model roadmaps into executable engineering deliverables.
• Collaborate with Data Engineering to ensure consistent, high-quality data feeds into ML pipelines.
• Communicate delivery status, risks, and trade-offs clearly to stakeholders and leadership.
• Manage, mentor, and develop a team of ML Ops engineers across experience levels.
• Set clear expectations around quality, delivery discipline, and operational ownership.
• Foster a culture of automation, documentation, and continuous improvement.

Skills

CI/CD pipelines

Containerization

Orchestration

Model registries

Monitoring tools

Azure

Databricks

Stakeholder management

Team leadership

Tools

Docker

Kubernetes

Job description

Company Description

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.

Position Overview

The ML Ops Engineering Manager is responsible for leading the delivery and operational excellence of Ralph Lauren's ML Ops capability - the infrastructure, pipelines, and practices that take machine learning and AI models from development into reliable, governed production use. This role manages a team of ML Ops engineers and partners closely with Data Science, Data Engineering, Platform, and Governance teams to deliver scalable, secure, and well-monitored model deployment and operations across Ralph Lauren's growing AI portfolio. The ML Ops Engineering Manager focuses on execution, engineering rigor, team leadership, and cross-functional coordination, while model strategy and prioritization remain with Data Science and AI leadership.

Essential Duties & Responsibilities

What you will be doing (responsibilities):

1. ML Ops Delivery Leadership
   • Lead end-to-end delivery of CI/CD pipelines, model registry practices, and deployment infrastructure for ML and AI use cases.
   • Drive predictable execution of the ML Ops roadmap in partnership with Data Science and Platform leadership.
   • Establish and enforce engineering standards for model packaging, testing, deployment, and rollback.
   • Proactively manage delivery risks, technical dependencies, and production incidents across deployed models.
2. Platform & Architecture Alignment
   • Ensure ML Ops solutions align with enterprise data and AI platform standards and architecture patterns.
   • Partner with platform and architecture teams to design scalable, cost-effective serving and training infrastructure.
   • Guide teams on appropriate use of shared compute, environments, and model infrastructure.
3. Monitoring, Governance & Trust
   • Embed model monitoring, drift detection, and performance alerting into pipelines as standard practice.
   • Ensure model versioning, lineage, and documentation requirements are met to support auditability.
   • Partner with data governance, security, and compliance teams to ensure responsible and compliant AI deployment.
4. Engineering Excellence & Operational Readiness
   • Drive CI/CD maturity for ML pipelines, including automated testing, staged rollouts, and controlled promotions.
   • Ensure deployed models are operationally ready with monitoring, alerting, and clear incident ownership.
   • Continuously improve reliability, latency, and cost-efficiency of training and inference workloads.
5. Stakeholder & Cross-Functional Collaboration
   • Partner with Data Science and AI leadership to translate model roadmaps into executable engineering deliverables.
   • Collaborate with Data Engineering to ensure consistent, high-quality data feeds into ML pipelines.
   • Communicate delivery status, risks, and trade-offs clearly to stakeholders and leadership.
6. People Leadership & Team Development
   • Manage, mentor, and develop a team of ML Ops engineers across experience levels.
   • Set clear expectations around quality, delivery discipline, and operational ownership.
   • Foster a culture of automation, documentation, and continuous improvement.

Experience, Skills & Knowledge

What you bring (Qualifications): Required

• 7-10 years of experience in MLOps, ML engineering, DevOps, or platform engineering, including team or delivery leadership.
• Strong hands-on background in CI/CD, containerization, and orchestration for ML workloads.
• Experience operating model registries, monitoring tooling, and ML pipelines in enterprise environments.
• Working knowledge of cloud platforms (Azure preferred) and Databricks-based ecosystems.
• Strong stakeholder management skills across data science, engineering, platform, and governance teams.

Preferred

• Experience supporting AI/ML programs in retail, consumer goods, or other data-intensive industries.
• Familiarity with LLM/GenAI deployment patterns and evaluation practices.
• Exposure to enterprise data governance and AI risk/compliance frameworks.

Success Measures

• Predictable, governed delivery of ML Ops capabilities with reduced rework.
• Improved model deployment reliability, observability, and incident response.
• Increased reuse of standardized deployment patterns across model teams.
• High stakeholder confidence in the reliability and execution of the ML Ops function.