DeepRec.ai
This range is provided by DeepRec.ai. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
Base pay range
CA$125,000.00 / yr – CA$135,000.00 / yr
Co-Founder and Managing Director USA @ DeepRec.ai
Senior MLOps Engineer – Real-Time AI & Video Applications (Remote or On-Site or Hybrid)
Office Location : Toronto
Job Type : Full-time
We’re hiring for an impressive AI company focused on real-time AI and Video Applications. Their team is made up of leading experts in computer graphics and generative modeling, and they are on a rapid growth trajectory. We’re looking for experienced MLOps Engineers that want to work on real-time AI applications that are shaping the future of media.
The Role
We’re looking for a talented MLOps Engineer to build and maintain robust machine learning pipelines and infrastructure. You’ll be working closely with AI researchers, data scientists, and software engineers to deploy state-of-the-art models into production, optimize real-time inference, and ensure systems scale effectively.
What You’ll Do
- Design and optimize ML pipelines for training, validation, and inference
- Automate deployment of deep learning and generative models for real-time use
- Implement versioning, reproducibility, and rollback capabilities
- Deploy and manage containerized ML solutions on cloud platforms (AWS, GCP, Azure)
- Optimize model performance using TensorRT, ONNX Runtime, and PyTorch
- Work with GPUs, distributed computing, and parallel processing to power AI workloads
- Build and maintain CI / CD pipelines using tools like GitHub Actions, Jenkins, ArgoCD
- Automate model retraining, monitoring, and performance tracking
- Ensure compliance with privacy, security, and AI ethics standards
What You Bring
- 3+ years of experience in MLOps, DevOps, or AI model deployment
- Strong skills in Python and frameworks like TensorFlow, PyTorch, ONNX
- Proficiency with Docker, Kubernetes, and serverless architectures
- Hands-on experience with ML tools (ArgoWorkflow, Kubeflow, MLflow, Airflow)
- Experience deploying and optimizing GPU-based inference (CUDA, TensorRT, DeepStream)
- Solid grasp of CI / CD practices and scalable ML infrastructure
- Passion for automation and clean, maintainable system design
- Strong understanding of distributed systems
- Bachelor’s or Master’s in Computer Science or equivalent work experience
Bonus Skills
- Experience with CUDA programming
- Exposure to LLMs and generative AI in production
- Familiarity with distributed computing (Ray, Horovod, Spark)
- Basic networking knowledge
Please apply now for more details and next steps. We look forward to hearing from you.
Seniority level
Mid-Senior level
Employment type
Full-time
Technology, Information and Media, Information Services, and Consumer Services
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