Senior MLOps Engineer

ZipRecruiter; Toronto; Hybrid; CAD 100,000 - 130,000; Full time

Job summary

A leading AI company in Toronto is seeking a Senior MLOps Engineer to build and maintain robust machine learning pipelines. The role involves collaborating with AI researchers and software engineers to deploy state-of-the-art models, optimize performance, and ensure scalability. Candidates should have strong skills in Python and experience with ML tools and cloud platforms.

Qualifications

• 3+ years of experience in MLOps, DevOps, or AI model deployment.
• Strong skills in Python and frameworks like TensorFlow, PyTorch, ONNX.

Responsibilities

• Design and optimize ML pipelines for training, validation, and inference.
• Automate deployment of deep learning and generative models for real-time use.

Skills

Python

Automation

Distributed Systems

Education

Bachelor’s or Master’s in Computer Science

Tools

Docker

Kubernetes

TensorFlow

PyTorch

MLflow

Airflow

GitHub Actions

Jenkins

ArgoCD

Job description

Job Description

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 who are focussed 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)
• Edge AI deployment experience (Triton Inference Server, TFLite, CoreML)
• Basic networking knowledge

Please apply now for more details and next steps

We look forward to hearing from you