Machine Learning Engineer

Job Title: MLOps Engineer Work Arrangement: Remote Location: Toronto, Canada Salary: Up-to $125,000 CAD MLOps Engineer – Real-Time AI Systems 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 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. Deploy models on AWS, GCP, or Azure using Docker and Kubernetes. Optimize real-time inference using TensorRT, ONNX Runtime, or PyTorch. Use GPUs, distributed systems, and parallel computing for performance. Create CI/CD workflows (GitHub Actions, Jenkins, ArgoCD) for ML. Automate model retraining, validation, and monitoring. Address data drift, latency, and compliance concerns. What You Bring 3 years in MLOps, DevOps, or model deployment roles. Strong Python and experience with ML frameworks (PyTorch, TensorFlow, ONNX). Proficiency with cloud platforms, Docker, and Kubernetes. Experience with ML tools like MLflow, Airflow, Kubeflow, or Argo. Knowledge of GPU acceleration (CUDA, TensorRT, DeepStream). Understanding of scalable, low-latency ML infrastructure. Nice to Have Experience with Ray, Spark, or edge AI tools (Triton, TFLite, CoreML). Basic networking knowledge or CUDA programming skills.