Requirements

- 8+ years of experience building production ML or data platforms
- A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field
- 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
- Solid understanding of MLOps best practices: CI/CD, monitoring, reproducibility, and security
- Experience supporting multiple ML teams in a shared platform workplace
- Active ownership of orphaned problems and a willingness to assimilate missing knowledge to get the job done

What the job involves

- Design, improve, and operate a scalable ML platform to accelerate model training, deployment, and governance
- Serve as the technical bridge between data science and production engineering
- Join a small, critical team that scales Faire’s ability to support tens of thousands of local businesses in a constantly narrowing retail landscape
- 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 how to utilize the ML platform to advance development from notebook to production for our most critical models
- 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 (e.g., GitHub Actions)
- Optimize performance, reliability, and cost across training and inference workloads
- Configure IAM and RBAC for sensitive data sets
- Establish observability for data quality, model performance, and platform health
- Build and maintain ML Platform technical documentation

Tech Stack

- Languages: Python, SQL, Kotlin
- ML Frameworks: PyTorch, MLFlow
- Big Data & Processing: Spark, Kafka, Databricks, Snowflake, Fivetran, Iceberg, Unity Catalog, Datadog, Airflow, Cockroach DB, MySQL
- Cloud & Infrastructure: AWS, S3, SageMaker, Kubernetes, Docker, GitHub Actions, Terraform
- Generative AI: Claude Sonnet 4.5, ChatGPT 5.2

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📌 Staff Machine Learning Platform Engineer (Toronto)
🏢 Faire
📍 Toronto