lululemon
Website:
lululemon.com
Job details:
Who we are:
Founded in 1998 at Vancouver, lululemon is a performance and lifestyle product company that create transformational products and experiences that build meaningful connections, unlocking greater possibility and wellbeing for all. We are driven by our brand purpose to elevate human potential by making individuals feel their best which helps us design our products with high filter and high style. We use a unique product creation methodology called Science of Feel in all our products to offer convenient, comfortable, and long-lasting experience. We owe our success to our innovative products, commitment to our people, and the incredible connections we make in every community we're in.
About this team:
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 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 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.
Core responsibilities:
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.
Select responsibilities include:
- 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
- Developing 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
- 8-12 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 (Aws/Azure)
Must haves:
- Acknowledge the presence of choice in every moment and take personal responsibility for your life.
- Possess an entrepreneurial spirit and continuously innovate to achieve great results.
- Communicate with honesty and kindness and create the space for others to do the same.
- Lead with courage, knowing the possibility of greatness is bigger than the fear of failure.
- Foster connection by putting people first and building trusting relationships.
- Integrate fun and joy as a way of being and working, aka doesn’t take yourself too seriously.
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