Senior Software Engineer - Data and AI platforms

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.

Core responsibilities:

As a Senior Software Engineer, you will design and build data systems, AI-enabled platform capabilities, and enterprise solutions that support multiple product teams, contributing to architectural patterns for how enterprise knowledge is modeled, connected, and consumed. You will work at the intersection of Data (logical and semantic), AI/LLM retrieval and grounding, and distributed systems and APIs.

You will drive technical decisions with broad impact across multiple teams including architecture patterns and technology selections, conduct high-impact code reviews focusing on system design and long-term maintainability, and establish engineering standards and best practices adopted across the domain including testing strategies and deployment procedures.
You will mentor engineers at multiple levels providing guidance on advanced technical concepts and career development, partner with product and business stakeholders on technical strategy and roadmap planning, and represent engineering perspective in cross-functional initiatives while leading incident response for critical production issues. This role may require availability outside of standard business hours, including on?call support, based on business needs.

Select responsibilities include:

• Contribute to and help evolve canonical semantic models (taxonomies, ontologies, knowledge graphs) for enterprise domains such as product, customer, and supply chain
• Build and maintain systems that provide high-quality context for LLMs, including Retrieval pipelines (RAG / GraphRAG), Semantic search (embeddings, hybrid search), and evidence-based reasoning layers
• Collaborate with AI/ML Science teams to validate model correctness, reduce hallucination risk, and improve trust in AI-generated outputs
• Identify and address challenges of data ambiguity, duplication, and inconsistencies through data validation, schema governance, and versioning practices
• Lead implementation of complex systems and features spanning multiple services within established architectural frameworks and make decisions with minimal supervision
• Write exemplary code demonstrating engineering best practices in critical systems as technical reference for teams
• Establish and enforce coding standards, testing practices, and engineering best practices across multiple teams in domain
• Provide technical input and implementation perspectives to architects on system design and integration approaches
• Lead code quality and design reviews across teams ensuring technical alignment with organizational standards

Qualifications:

• Bachelors degree in Computer Science, Software Engineering, or related technical field; Masters degree beneficial, or equivalent experience
• 8-12 years of software development experience, building complex systems and driving technical initiatives across teams, or equivalent
• Proven track record of writing exemplary code that serves as a technical reference; experience establishing coding standards and quality practices adopted across a domain
• Demonstrated ability to decompose highly complex problems including algorithmic challenges into scalable solutions; deep understanding of system-wide implications of technical decisions across multiple services
• Experience designing integration solutions for complex domain areas; track record of driving integration implementation decisions across teams within architectural standards
• Experience building knowledge bases in retail or similar domains
• Familiarity with search, recommender and RAG/GraphRAG hybrid retrieval architectures
• Experience defining enterprise semantic layers used across multiple product teams
• Exposure to AI-Driven systems optimization, predictions and Agents
• Experience working with knowledge representation for LLM-powered applications