
1001 - 5000 employees
Founded 2017
🤝 B2B
🛍️ eCommerce
đź›’ Retail
B2B • eCommerce • 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.
đź•’ August 14
🇨🇦 Canada – Remote
đź’µ $216k - $297k / year
⏰ Full Time
đź”´ Lead
🏗️ Platform Engineer
đź‘» Ghost score 6%
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1001 - 5000 employees
Founded 2017
🤝 B2B
🛍️ eCommerce
đź›’ Retail
B2B • eCommerce • 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.
• Design and operate ML infrastructure, including workspaces, clusters, jobs, and workflows • Productionize ML workloads using Spark, Delta Lake, MLflow, and Databricks Workflows • Teach data scientists to use the ML platform and advance models from notebook to production • Implement Unity Catalog for data governance, lineage, access control, and secure multi-tenant usage • Build CI/CD pipelines for ML using Terraform and Git-based workflows such as GitHub Actions • Optimize performance, reliability, and cost across training and inference workloads • Configure IAM and RBAC for sensitive datasets • Establish observability for data quality, model performance, and platform health • Build and maintain ML Platform technical documentation
• 8+ years of experience building production ML or data platforms • A degree in Computer Science, Engineering, Statistics, or a related technical field; graduate level is preferred • Strong hands-on expertise with Databricks, Spark, Delta Lake, and MLflow • Proficiency in Python, SQL, and distributed systems concepts • Experience with cloud platforms and infrastructure-as-code • Understanding of MLOps best practices, including CI/CD, monitoring, reproducibility, and security • Experience supporting multiple ML teams in a shared platform environment • Ability to take ownership of orphaned problems and acquire missing knowledge to complete the work • Proficiency with relevant technologies including Kotlin, PyTorch, Kafka, Snowflake, Airflow, AWS, Kubernetes, Docker, and Terraform
• Equity • Comprehensive benefits • Competitive pay • Latest enterprise AI tools • Flexible remote work option for candidates located in Ontario, Canada
Apply Nowđź•’ July 22
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