Machine Learning Engineer II

This is a fully remote position, open to applicants in Canada.

📋 Description

• Take charge of the design and execution of contemporary AI stack components, which include data ingestion for AI/ML tasks as well as complete model training and serving pipelines.

• Develop and oversee resilient AI platforms that are cost-effective. You will strike a balance between maintaining legacy models and the swift development of advanced, scalable solutions.

• Offer technical guidance to junior engineers and nurture a collaborative atmosphere. You will serve as a liaison between data science and production engineering.

• Advocate for best practices in coding, testing, and MLOps. You excel in uncertain situations by autonomously identifying opportunities to enhance model pipelines and optimize AI workflows.

• Collaborate with data scientists, product managers, and software engineers to convert business requirements into technical specifications and integrate AI solutions into production applications.

• Uphold model quality standards, integrity, and reliability. You will be accountable for implementing model lineage, fairness, and privacy controls within automated pipelines.

• Create monitoring frameworks to assess model performance and system KPIs, ensuring that our AI initiatives yield measurable business results.

⛳️ Requirements

• A minimum of 4–6 years of professional experience in machine learning engineering, with a verified history of deploying models in production settings.

• A Degree or Diploma in Computer Science, Engineering, Data Science, Applied AI, Machine Learning, or a relevant combination.

• A comprehensive understanding of the modern AI stack, including data ingestion workflows, and experience with curated data warehouses such as Snowflake, Databricks, or Redshift.

• At least 3 years of practical experience with AWS infrastructure, specifically SageMaker, Spark/AWS Glue, and Infrastructure as Code (IaC) using Terraform.

• High proficiency in managing multi-stage workflows utilizing Airflow or similar orchestration systems to automate training and deployment processes.

• Practical experience with MLflow, Kubeflow, or SageMaker Feature Store to facilitate the complete machine learning lifecycle.

• Familiarity with model governance practices (lineage, fairness, and privacy) and experience with data cataloging tools for compliance purposes.

• Strong capability to convey complex technical ideas to non-technical stakeholders and influence the direction of projects.

• Experience in FinTech or SaaS environments is a significant advantage.

🏝️ Benefits

• Bonus Structure

• Employer-paid Benefits Plan

• Health & Wellness Flex Account

• Professional Development Account

• Wellness Days

• Paid Holiday Shutdown

• Wave Days (extra vacation days in the summer)

• Get A-Wave Program (work from anywhere in the world up to 90 days)