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 Software Engineer, you will build and maintain data systems and AI-enabled platform capabilities that support product teams, working within established architectural patterns for how enterprise knowledge is modeled, connected, and consumed. You will develop skills and experience at the intersection of Data (logical and semantic), AI/LLM retrieval and grounding, and distributed systems and APIs.
You will lead technical design discussions presenting proposals and trade-offs, conduct thorough code reviews ensuring quality and maintainability standards, and mentor junior engineers through pair programming and technical guidance on coding practices and problem-solving approaches. You will collaborate closely with product managers and stakeholders on technical feasibility and scope definition, make technical decisions for projects including technology choices and implementation strategies, and contribute to team engineering practices and standards that improve code quality and development velocity.

This role may require availability outside of standard business hours, including on call support, based on business needs.

Select responsibilities include:

• Implement and maintain components of canonical semantic models (taxonomies, ontologies, knowledge graphs) for enterprise domains such as product, customer, and supply chain
• Build and support systems that provide high-quality context for LLMs, including Retrieval pipelines (RAG / GraphRAG), Semantic search (embeddings, hybrid search), and evidence-based reasoning layers
• Work alongside AI/ML Science teams to test and validate model outputs, surfacing issues that contribute to hallucination risk or reduced trust in AI-generated outputs
• Identify and escalate challenges related to data ambiguity, duplication, and inconsistencies, and implement fixes in line with established schema governance and versioning practices
• Design and implement technical solutions for features and systems within established architectural frameworks including component design, API contracts, and data models
• Write high-quality, scalable code across the full technology stack following software engineering best practices
• Lead technical design discussions on implementation approaches presenting proposals and facilitating team alignment
• Conduct thorough code reviews ensuring adherence to standards, identifying security vulnerabilities, and providing mentorship through feedback
• Implement comprehensive automated testing strategies including unit, integration, and end-to-end tests to ensure quality

Qualifications:

• Bachelors degree in Computer Science, Software Engineering, or related technical field, or equivalent experience
• 4-8 years of software development experience designing scalable systems and leading technical implementation, or equivalent
• Proven experience designing and implementing features with high-quality, well-tested code; track record of leading code reviews and providing constructive mentorship through feedback
• Demonstrated ability to decompose complex problems into implementable solutions; experience analysing how technical decisions propagate across distributed systems
• Experience designing and implementing complex integrations ensuring reliable inter-system communication; familiarity with presenting integration approaches and architectural trade-offs
• Experience building knowledge bases in retail or similar domains
• Familiarity with search, recommender and RAG/GraphRAG hybrid retrieval architectures
• Exposure to AI-driven systems - optimization, predictions and Agents
• Experience working with knowledge representation for LLM-powered applications