MLOps Engineer: Scalable AI Pipelines & Profit Sharing

Joinimagine; Toronto; On-site; CAD 145,000 - 155,000; Full time

Benefits offered by this job

Health & Dental Benefits

Wellness & Training programs

RRSP Matching

Profit Sharing

Team lunches & social activities

Job summary

ModiFace, a leader in AR/AI beauty tech, seeks an experienced MLOps Developer to scale our ML pipelines in a cloud-native environment. You will automate stages of the AI/ML lifecycle and contribute to high-throughput inference in production.

Join a collaborative team and help integrate LLMs and ML tooling while adhering to Agile/SCRUM practices. The role is grounded in Toronto, with a strong emphasis on scalable infrastructure and performance.

Qualifications

• 3+ years hands-on MLOps in a cloud-native environment.
• Strong Python software engineering background.
• Deep Docker and Kubernetes experience.
• GCP experience strongly preferred.
• Solid knowledge of ML lifecycle tools (MLflow, DVC).
• Familiarity with Agile/SCRUM processes.
• Excellent communication for non-technical audiences.

Responsibilities

• Automate all stages of the AI/ML pipeline in a cloud environment.
• Design and maintain ML-focused CI/CD pipelines and artifacts.
• Provision and scale cloud infrastructure (primarily GCP).
• Optimize deep learning models for web and mobile deployment.
• Integrate LLMs and domain APIs into structured tools.
• Promote software development best practices through reviews and automated tests.
• Collaborate across teams to industrialize ML models and foster continuous learning.

Skills

MLOps

Python

Agile/SCRUM

Communication

ML Fundamentals

Education

Bachelor’s or Master’s in CS/SE or related

Tools

Docker

Kubernetes

GCP

MLflow

DVC

Job description

ModiFace, a leader in AR/AI beauty tech, seeks an experienced MLOps Developer to scale our ML pipelines in a cloud-native environment. You will automate stages of the AI/ML lifecycle and contribute to high-throughput inference in production.

Join a collaborative team and help integrate LLMs and ML tooling while adhering to Agile/SCRUM practices. The role is grounded in Toronto, with a strong emphasis on scalable infrastructure and performance.