Senior AI​/ML Engineer

Overview

lululemon is an innovative performance apparel company for yoga, running, training, and other athletic pursuits. We create transformational products and experiences that support people in moving, growing, connecting, and being well. We focus on creating positive change to build a healthier, thriving future with an equitable, inclusive and growth‑focused environment.

The Enterprise Data & AI team is a strategic and operational driver of growth for lululemon, owning and building the data and AI platforms and products that enable the enterprise to operate with intelligence  team leads the design and delivery of a trusted unified data foundation, advanced analytics capabilities, and AI solutions across lululemon’s vertically integrated retail ecosystem, embedding strong data governance and responsible AI practices from the very beginning.

By applying AI to critical business challenges and creating new, transformative AI solutions, the team helps reshape how lululemon operates. Through deep partnership with product, technology, and business teams, Enterprise Data & AI accelerates product innovation, unlocks measurable value, elevates guest and educator experiences, and drives enterprise efficiency.

As a Senior AI/ML Engineer, you will lead the delivery of scalable AI/ML solutions to business problems. You will build, deploy, scale and maintain AI/ML solutions. You will apply engineering best practices, implement rigorous evaluation frameworks, and design MLOps and observability standards. You will be the technical authority for ML engineering challenges from setting up model training and fine‑tuning to architectures and system design for serving AI/ML inference solutions in production.

You will help drive AI/ML engineering excellence through mentorship, design reviews, and platform investment. In this role, you will own technical delivery and partner with applied scientists, software engineers, and product teams to realize AI capabilities into production.

• Lead delivery of applied AI/ML solutions, including data pipelines, model training and experimentation infrastructure, evaluation systems, production-ready pipelines and APIs, and ML Ops for monitoring models or solutions in production.
• Define ML engineering standards for model development, evaluation, and deployment; implement reusable training pipeline templates.
• Design and implement model evaluation systems and tooling including benchmark suites, human evaluation workflows, and online experiment platforms in partnership with applied science teams.
• Lead architecture and engineering of LLM and GenAI systems including RAG pipelines, fine-tuning infrastructure, and agentic frameworks.
• Build and maintain AI observability frameworks covering model performance, data drift, training health metrics, and responsible AI monitoring.
• Build and operate distributed training pipelines for advanced ML and GenAI models.
• Implement scalable model serving architectures for real-time and batch inference.
• Develop reusable MLOps components to support experimentation, deployment, monitoring, and rollback.
• Partner with AI/ML scientists to product ionize models while meeting accuracy, performance, reliability, and responsible AI requirements.

• Bachelor's or Master’s degree in computer science, machine learning, or related technical field;
  Master’s or equivalent experience beneficial.
• 6-10 years of experience building and delivering AI/ML solutions into production.
• Demonstrated ability to define software engineering standards for AI/ML systems across the domain including code quality, testing requirements, service design patterns, and API contract guidelines.
• Demonstrated ability to define model implementation and training standards including architecture patterns, evaluation criteria, and responsible AI assessment frameworks adopted across the domain.
• Demonstrated ability to define ML Ops platform standards and reusable deployment templates adopted across the domain.
• Experience with common ML tools and frameworks and implementation such as Python, Spark, Airflow, MLFlow, feature stores, cloud ML platforms.