Senior MLOps Engineer

DeepRec.ai; Toronto; On-site; CAD 125,000 - 135,000; Full time

Job summary

A leading AI company is seeking a Senior MLOps Engineer to build and maintain machine learning pipelines and infrastructure. The ideal candidate will work closely with AI researchers and data scientists to deploy state-of-the-art models, optimizing performance for real-time applications. This innovative organization drives advancements in video applications and offers a collaborative environment for skilled professionals looking to shape the future of media.

Qualifications

• 3+ years of experience in MLOps, DevOps, or AI model deployment.
• Hands-on experience with ML tools and automation focus.
• Strong grasp of scalable ML infrastructure.

Responsibilities

• Design and optimize ML pipelines for training, validation, and inference.
• Automate deployment of deep learning models for real-time use.
• Deploy and manage containerized ML solutions on cloud platforms.

Skills

Python

TensorFlow

PyTorch

Docker

Kubernetes

CI / CD practices

Distributed systems

Automation

Education

Bachelor’s or Master’s in Computer Science

Tools

TensorRT

ONNX

GitHub Actions

Jenkins

ArgoCD

ArgoWorkflow

Kubeflow

MLflow

Airflow

Job description

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

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