Machine Learning Engineer

DeepRec.ai; Toronto; Remote; CAD 100,000 - 125,000; Full time

Job summary

DeepRec.ai is seeking an experienced MLOps Engineer to deploy and scale cutting-edge ML models for real-time applications. In this full-time remote role, you will work with data scientists and engineers to build automated ML infrastructure, ensuring optimal performance and compliance.

Qualifications

• 3+ years in MLOps, DevOps, or model deployment roles.
• Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, ONNX).
• Proficiency with cloud platforms, Docker, and Kubernetes.

Responsibilities

• Build and manage ML pipelines for training, validation, and inference.
• Automate deployment of deep learning and generative AI models.
• Ensure model versioning, rollback, and reproducibility.

Skills

Python

MLOps

DevOps

Machine Learning

Cloud Platforms

Docker

Kubernetes

GPU Acceleration

Tools

MLflow

Airflow

Kubeflow

Argo

Job description

Direct message the job poster from DeepRec.ai

Recruitment Consultant - Hiring Robotics, Computer Vision & Autonomy Talent In The U.S

Job Title: MLOps Engineer

Work Arrangement: Remote

Location: Toronto, Canada

Salary: Up-to $125,000 CAD

We're looking for an experienced MLOps Engineer to help deploy and scale cutting-edge ML models for real-time video and audio applications. You'll work alongside data scientists and engineers to build fast, reliable, and automated ML infrastructure.

Key Responsibilities

1. Build and manage ML pipelines for training, validation, and inference.
2. Automate deployment of deep learning and generative AI models.
3. Ensure model versioning, rollback, and reproducibility.
4. Deploy models on AWS, GCP, or Azure using Docker and Kubernetes.
5. Optimize real-time inference using TensorRT, ONNX Runtime, or PyTorch.
6. Use GPUs, distributed systems, and parallel computing for performance.
7. Create CI/CD workflows (GitHub Actions, Jenkins, ArgoCD) for ML.
8. Automate model retraining, validation, and monitoring.
9. Address data drift, latency, and compliance concerns.

What You Bring

1. 3+ years in MLOps, DevOps, or model deployment roles.
2. Strong Python skills and experience with ML frameworks (PyTorch, TensorFlow, ONNX).
3. Proficiency with cloud platforms, Docker, and Kubernetes.
4. Experience with ML tools like MLflow, Airflow, Kubeflow, or Argo.
5. Knowledge of GPU acceleration (CUDA, TensorRT, DeepStream).
6. Understanding of scalable, low-latency ML infrastructure.

Nice to Have

1. Experience with Ray, Spark, or edge AI tools (Triton, TFLite, CoreML).
2. Basic networking knowledge or CUDA programming skills.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Information Services and Technology, Information and Media

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