Backend Python Engineer with Machine Learning

Smooth Commerce; Toronto; Remote; CAD 115,000; Full time

Job summary

A dynamic B2B SaaS firm in Canada is seeking a Backend Python Engineer with expertise in machine learning to develop scalable infrastructure and deployment pipelines. The ideal candidate will have 2-5 years of experience, proficiency in MLOps tools, and a solid understanding of the full ML lifecycle. This position allows for remote work with a focus on collaboration and documentation.

Benefits

Full benefits

Professional growth opportunities

Dynamic work environment

Qualifications

• 2-5 years experience in machine learning.
• Proven experience in designing and implementing CI / CD pipelines.
• Practical experience building and managing secure ML environments in AWS, GCP, or Azure.

Responsibilities

• Establish secure cloud-based machine learning environments.
• Construct automated pipelines for data preparation and model validation.
• Implement CI / CD workflows for model deployment.

Skills

MLOps

CI / CD Expertise

Cloud Infrastructure

Machine Learning Fundamentals

Communication & Collaboration

Documentation & Knowledge Transfer

Tools

Docker

Kubernetes

Terraform

MLflow

Apache Airflow

Job description

Overview

Salary: $115,000 Annually

Role: Backend Python Engineer with Machine Learning

Compensation: $115,000 plus incentives

Milestone stock options

Perks: Client perks. Full benefits.

About Smooth Commerce :

Smooth Commerce is a dynamic B2B SaaS platform specializing in first-party white-label web and application-based digital ordering, customer marketing, and loyalty systems. Our primary market is the restaurant technology sector in the United States and Canada. Our mission is to empower our clients to increase sales, transition transactions to more cost-effective platforms, and leverage data-driven insights for prosperity. We are committed to continuous innovation and collaboration to address the evolving digital requirements of our clientele.

Role Summary

The Backend Python Engineer with Machine Learning will spearhead the development of infrastructure and deployment pipelines for model development, training, and production. This includes the creation of MLOps tools and pipelines to facilitate experimentation, governance, and scalable deployment across diverse environments.

Timeframe

Full Time

Location

Remote, Canada-based Greater Toronto Area preferred

Reporting To: Platform Architect and AI Architect

Willing to meet in person to collaborate; strong verbal and written communication skills; experience working in small fast-paced environments.

Key Responsibilities

• Establish secure, cloud-based machine learning development environments (AWS, GCP, or Azure).
• Construct automated pipelines for data preparation, training, and model validation (e.g., MLflow, Airflow).
• Implement CI / CD workflows for deploying models to staging and production environments (e.g., Docker, Kubernetes, Terraform).
• Develop and integrate monitoring and retraining triggers based on model drift, performance degradation, or newly acquired data.
• Conduct evaluations comparing baseline and experimental models using metrics such as AUC, recall, precision, and F1-score.
• Collaborate with the Architect to ensure the modularity and extensibility of pipeline design.
• Provide internal team enablement through knowledge transfer and comprehensive technical documentation.

Deliverables

• CI / CD-enabled model training pipeline
• Model evaluation reports and logs
• Monitoring dashboard or integration (e.g., Prometheus, Seldon Core)
• DevOps handover documentation for internal ownership

Justification

Specialized infrastructure expertise is required to establish secure and scalable machine learning environments. This contractor will expedite deployment readiness and facilitate a sustainable handoff to internal teams.

Key Qualifications

• 2-5 Years Experience
• MLOps and CI / CD Expertise: Proven experience in designing and implementing CI / CD pipelines for machine learning models, including containerization and deployment using tools such as Docker, Kubernetes, and Terraform. Hands-on knowledge of MLOps platforms such as MLflow for experiment tracking and model management, along with workflow orchestration tools like Apache Airflow, Kubeflow, or equivalent. Strong background in monitoring and maintaining production models, including performance tracking, drift detection, and automated retraining strategies.
• Cloud Infrastructure: Practical experience building and managing secure, scalable machine learning environments on cloud platforms such as AWS, GCP, or Azure.
• Machine Learning Fundamentals: Proficiency in model evaluation using standard metrics including AUC, recall, precision, and F1-score. Solid understanding of the full machine learning lifecycle—from data preparation and model training to validation, deployment, and pipeline optimization.
• Communication & Collaboration: Works closely with a Platform Architect and a PM. Must clearly communicate technical concepts and collaborate on design for modularity and scalability.
• Documentation & Knowledge Transfer: A key deliverable is handover to an internal team. Must create clear, comprehensive technical documentation and be adept at teaching / enabling others to ensure project sustainability.

What We Offer

Smooth Commerce provides a dynamic and inclusive work environment with opportunities for professional growth and development. We value innovation, collaboration, and a client-centric approach. If you are looking to advance your career in project management within a forward-thinking company, we would love to hear from you.

We thank all applicants for their interest; however, only those selected for interviews will be contacted.