
1001 - 5000 employees
Founded 2017
đ€ B2B
đïž eCommerce
đ Retail
B2B âą eCommerce âą Retail
Faire is a wholesale marketplace that connects independent retailers with unique products from global brands. It offers a platform for retailers to discover and purchase products that resonate with their customers, featuring various categories including women-owned, eco-friendly, and handmade items. With benefits like easy returns on first orders and flexible payment options, Faire aims to simplify the purchasing experience for retailers while supporting diverse and local brands.
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1001 - 5000 employees
Founded 2017
đ€ B2B
đïž eCommerce
đ Retail
B2B âą eCommerce âą Retail
Faire is a wholesale marketplace that connects independent retailers with unique products from global brands. It offers a platform for retailers to discover and purchase products that resonate with their customers, featuring various categories including women-owned, eco-friendly, and handmade items. With benefits like easy returns on first orders and flexible payment options, Faire aims to simplify the purchasing experience for retailers while supporting diverse and local brands.
âą Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows âą Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows âą Teach data scientists to use the ML platform and advance models from notebook to production âą Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage âą Build CI/CD pipelines for ML using Terraform and Git-based workflows such as GitHub Actions âą Optimize performance, reliability, and cost across training and inference workloads âą Configure IAM and RBAC for sensitive datasets âą Establish observability for data quality, model performance, and platform health âą Build and maintain ML Platform technical documentation
âą 8+ years of experience building production ML or data platforms âą A degree in Computer Science, Engineering, Statistics, or a related technical field; graduate level is preferred âą Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow âą Proficiency in Python, SQL, and distributed systems concepts âą Experience with cloud platforms and infrastructure-as-code âą Understanding of MLOps best practices, including CI/CD, monitoring, reproducibility, and security âą Experience supporting multiple ML teams in a shared platform environment âą Ability to take ownership of orphaned problems and acquire missing knowledge to complete the work âą Proficiency with relevant technologies including Kotlin, PyTorch, Kafka, Snowflake, Airflow, AWS, Kubernetes, Docker, and Terraform
âą Equity âą Comprehensive benefits âą Competitive pay âą Latest enterprise AI tools âą Flexible remote work option for candidates located in Ontario, Canada
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