Platform Engineer

IT Accel, Inc.; Toronto; On-site; CAD 80,000 - 110,000; Full time

Job summary

An established industry player is seeking a Platform Engineer with a passion for machine learning. This role focuses on developing and automating workflows that ensure the reliability and performance of machine learning models. You'll design CI/CD workflows, manage model lifecycles, and integrate cloud-agnostic solutions. This is a fantastic opportunity for someone who thrives in a dynamic environment and enjoys collaborating with cross-functional teams to enhance ML infrastructure. If you're ready to make a significant impact in the field of machine learning, this position is for you!

Qualifications

• Strong knowledge of machine learning principles and best practices.
• Experience with CI/CD pipelines and automated deployment strategies.

Responsibilities

• Design and manage CI/CD workflows for machine learning models.
• Automate model drift detection and manage re-training cycles.

Skills

Machine Learning Principles

CI/CD Workflows

Model Lifecycle Management

Automated Deployment Strategies

REST APIs

Tools

GitHub Actions

Monitoring Tools

Job description

About The Role

We are looking for a Platform Engineer with Machine Learning experience to drive the full lifecycle of machine learning models, from development to deployment and ongoing monitoring. This role requires strong experience in CI/CD workflows, model lifecycle management, and cloud-agnostic solutions. The ideal candidate will focus on building and automating ML workflows, ensuring seamless integration and performance of models over time.

Responsibilities

1. Design, build, and manage CI/CD workflows for machine learning models.
2. Develop and implement model lifecycle workflows, including training, deployment, and monitoring.
3. Automate model drift detection and manage re-training cycles to maintain accuracy.
4. Ensure cloud-agnostic deployment strategies and integrate with third-party model lifecycle tools.
5. Build self-service capabilities to streamline ML model management.
6. Work with trained models, integrate with REST APIs, and oversee model performance monitoring over time.
7. Manage and release artifacts related to machine learning models.
8. Collaborate with data scientists, engineers, and stakeholders to enhance ML infrastructure.

Requirements

1. Strong knowledge of machine learning principles and best practices.
2. Experience with CI/CD pipelines, GitHub Actions, and automated deployment strategies.
3. Hands-on experience with model lifecycle management, including retraining and drift detection.
4. Expertise in cloud-agnostic ML deployment solutions.
5. Familiarity with REST APIs and model serving architectures.
6. Knowledge of monitoring tools for tracking model performance over time.
7. Experience working with third-party models and self-service ML platforms.

This is a great opportunity for an ML Engineer who enjoys building scalable, automated workflows and ensuring machine learning models remain performant and reliable over time.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Other

Industries

• IT Services and IT Consulting