Senior Engineer II - DevOps- DTL [T500-22065]

About lululemon:

lululemon is an innovative performance apparel company for yoga, running, training, and other athletic pursuits. Setting the bar in technical fabrics and functional design, we create transformational products and experiences that support people in moving, growing, connecting, and being well. We owe our success to our innovative products, commitment to our people, and the incredible connections we make in every community we're in. As a company, we focus on creating positive change to build a healthier, thriving future. In particular, that includes creating an equitable, inclusive and growth-focused environment for our people. As we continue to elevate our shopping experience, our India Tech Hub plays a key role in expanding our technology capabilities in Product Systems, Merchandising and Planning, Digital Presence, distribution and logistics, and corporate systems. Our team in India works as an extension of the global team on projects of strategic importance.

Senior Engineer II DevOps

About this team:

lululemon Engineering is dedicated to building secure, reliable, and performant products for our guests and partners. We embrace the philosophies of Agile, DevOps, and SRE to accelerate our development process and provide the most enjoyable, inclusive, and supportive work environment possible. We believe our journey is more fun when it is collaborative, as we focus on the future instead of the past. As an Engineer, you will work as part of a global team supported by our business and architecture partners to help us collaboratively develop and deliver industry leading technology solutions that drive lululemons business goals.

Who You Are:

As a Senior Engineer, you will bring a high level of technical knowledge as well as strong mentoring abilities. You will be counted on as a leader in your technology space as you contribute to all areas of development and operations (pre-production to production). You will work closely with a Technology Manager, using your experience and knowledge to guide a team of Engineers though their day-to-day process, and provide a central escalation point for production concerns. You will be part of an Agile production release team and may perform on-call support functions as needed

Must haves:

• Acknowledges the presence of choice in every moment and takes personal responsibility for their life.
• Possesses an entrepreneurial spirit and continuously innovates to achieve great results.
• Communicates with honesty and kindness and creates the space for others to do the same.
• Leads with courage, knowing the possibility of greatness is bigger than the fear of failure.
• Fosters connection by putting people first and building trusting relationships.
• Integrates fun and joy as a way of being and working, aka doesnt take themselves too seriously.

Qualifications:

• Bachelors or advanced degree in Computer Science, Software Engineering, or a related field.
• Minimum 14+ years of experience in software development, with at least 5 years of hands-on DevOps experience.
• Excellent verbal and written communication skills with the ability to influence cross-functional engineering teams.

Core DevOps & Cloud Engineering:

• Strong expertise with build tools such as Ant or Maven.
• Hands-on experience with CI/CD tools including Jenkins or GitHub Actions.
• Deep knowledge of Cloud DevOps using AWS (CodeBuild, CodeDeploy, CodePipeline) or Azure DevOps.
• Expertise in containerization technologies including Docker and Docker Registry management.
• Strong experience with Terraform, Helm charts, and Infrastructure as Code practices.
• Proven experience deploying and maintaining Kubernetes clusters in production environments.
• Proficiency in scripting and automation using Python, Shell, or Groovy.

Data, Streaming & Observability:

• Basic proficiency with streaming and big data technologies such as Kafka, Spark, and Hadoop.
• Experience with artifact repositories like JFrog Artifactory.
• Experience with monitoring and alerting tools such as Prometheus, Grafana, Datadog, and PagerDuty.
• Familiarity with building high-availability, scalable, and fault-tolerant backend infrastructures.

AI/ML & MLOps Engineering:

• Experience applying DevOps principles to Machine Learning pipelines (MLOps).
• Hands-on experience with ML orchestration tools such as MLflow, Kubeflow, SageMaker Pipelines, or Vertex AI.
• Strong understanding of data pipelines, feature stores, data versioning, and experiment tracking.
• Experience building CI/CD/CT pipelines for ML model training, validation, and deployment.
• Proficiency deploying ML models using Docker, Kubernetes, SageMaker, or serverless platforms.
• Working knowledge of Python for automation, data prep, and ML pipeline integration.
• Experience with GPU/CPU cluster optimization and scalable infrastructure for model training/inference.
• Familiarity with vector databases, embedding pipelines, and model registries (e.g., Pinecone, Weaviate, Faiss).

Understanding of LLM-Ops, including:

• Fine-tuning pipelines
• RAG architecture
• model evaluation & prompt testing
• agent lifecycle management
• Experience with ML observability: model drift detection, data quality checks, performance monitoring, and automated retraining triggers.
• Ability to collaborate closely with Data Scientists and ML Engineers to move experiments into stable, production-grade systems.