Senior AI/ML Engineer

About the role

The Enterprise Data & AI team at lululemon is a strategic and operational driver of growth. They own and build the data and AI platforms and products that enable the enterprise to operate with intelligence at scale. The 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.

Core responsibilities

• 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 productionize models while meeting accuracy, performance, reliability, and responsible AI requirements.

Qualifications

• 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.

Additional notes

• Authorization to work in the United States is required for this role.
• Please note: Applicants must be authorized to work for ANY employer in the U.S. We are unable to sponsor or take over sponsorship of employment visa at this time for this role.
• The typical hiring range for this position is from $176,760-$232,000 annually; the base pay offered is based on market location and may vary depending on job-related knowledge, skills, experience, and internal equity.
• Compensation and benefits package includes extended health and dental benefits, mental health plans, paid time off, savings and retirement plan matching, generous employee discount, fitness & yoga classes, parenthood top-up, extensive catalog of development course offerings, and more.
• Workplace arrangement requires in-person collaboration and connection, with a minimum of 4 days per week onsite.