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Mlops Jobs in Quebec (NOW HIRING)

Dollarama, Inc.

Specialiste, IA

Mont-royal, QC · On-site

Comprehension de bonnes pratiques en MLOps. Baccalaureat ou maitrise en informatique, genie logiciel, intelligence artificielle ou domaine connexe. Experience pratique avec des projets AI/LLM.

Tecsys Inc.

Senior Developer (AI / ML)

Montreal, QC · On-site +1

Implement MLOps best practices, including model evaluation, monitoring, and deployment. * Develop scalable AI pipelines using vector databases and modern retrieval techniques. * Apply Operations ...

Accenture

Senior AI Lead -Snowflake

Montreal, QC · On-site

Operate MLOps / LLMOps pipelines with CI/CD across the ML and LLM lifecycle. * This role requires in-office presence in the local ATC office 3 days per week. Here's What You Need: * Bachelor's degree ...

BRP

Scientifique des données

Montreal, QC · On-site

Connaissance du cycle de vie MLOps complet : surveillance des modèles, détection des dérives et stratégies de réentraînement * Contributions à des projets libres ou publications témoignant ...

BRP

Data Scientist

Laval, QC · On-site

Knowledge of the full MLOps lifecycle: model monitoring, drift detection, and retraining strategies * Open-source contributions or publications reflecting thought leadership in data science or AI

New

BRP

Data Scientist

Montreal, QC

... and MLOps principles STAND OUT WITH... Experience building LLM-powered applications or proof-of-concepts using prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning approaches ...

BRP

Scientifique des données

Longueuil, QC · On-site

Connaissance du cycle de vie MLOps complet : surveillance des modèles, détection des dérives et stratégies de réentraînement * Contributions à des projets libres ou publications témoignant ...

New

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Showing results 21-40

All JobsMlops JobsMlops Jobs in Quebec

Mlops information

See Quebec salary details

$101.5K

$159.3K

$189.5K

How much do mlops jobs pay per year?

As of Aug 9, 2026, the average yearly pay for mlops in Quebec is $159,286.00, according to ZipRecruiter salary data. Most workers in this role earn between $150,500.00 and $173,000.00 per year, depending on experience, location, and employer.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

What are the key skills and qualifications needed to thrive as an MLOps engineer?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.
What are popular job titles related to Mlops jobs in Quebec? For Mlops jobs in Quebec, the most frequently searched job titles are:
What job categories do people searching Mlops jobs in Quebec look for? The top searched job categories for Mlops jobs in Quebec are:
What cities in Quebec are hiring for Mlops jobs? Cities in Quebec with the most Mlops job openings:
Mlops jobs near you
Infographic showing various Mlops job openings in Quebec as of August 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 78% In-person, 8% Hybrid, and 14% Remote job distribution, with an average salary of $159,286 per year, or $76.6 per hour.

Computer Vision/ML Engineer

Norbert Health

Montreal, QC • On-site

Full-time

Re-posted 17 days ago

Job description

The company

Norbert is building autonomous robots that deliver healthcare.

Our AI sensing platform mounts on mobile robots and does the work of a care team memberrounding on patients, capturing vitals without contact (FDA-cleared for pulse and respiratory rate, more in the pipeline), running assessments, documenting to the EMR, and escalating when something's wrong. Autonomously.

We're not building demos. We're deployed in real facilities today, monitoring hundreds of patients daily. We're solving one of healthcare's hardest problems: a global nursing shortage that will hit 40% by 2030.

We're a small, international team backed by top-tier VCs, with offices in Brooklyn and Paris. We ship things that matter.

The position

We are looking for our lead deep learning engineer to spearhead the development of our groundbreaking sensing technology.

What you will do:
  • Design, fine-tune, and deploy computer vision models (YOLO, InsightFace, MediaPipe, facial landmark detection, object tracking, pose estimation) for real-time inference on the edge
  • Optimize models for embedded deployment using quantization, pruning, TensorRT, and NVIDIA Triton
  • Build and maintain MLOps pipelines for model training, validation, and performance monitoring
  • Develop video processing pipelines that integrate with both classical signal processing and ML based vital sign extraction
  • Establish engineering best practices and help reduce technical debt as we scale
  • Contribute to the architecture and implementation of the computer vision stack from research to production
What we look for:
  • Master's or PhD degree in Machine learning / Computer vision
  • Strong fundamentals: data structures, CV algorithms, and systems programming
  • Strong C++ skills - this is critical for our edge deployment pipeline
  • Solid Python proficiency for ML experimentation and tooling
  • Ability to work independently, solve complex problems, and drive projects to completion
  • 5+ years experience deploying computer vision models to production, ideally on resource-constrained devices
  • Experience with PyTorch and model optimization for edge AI
  • Proven ability to take models from research to production on embedded hardware

Nice to haves:

  • Experience with NVIDIA Jetson platform, TensorRT, or Triton Inference Server
  • MLOps experience (experiment tracking, model versioning, performance monitoring)
  • Experience with sensor fusion (RGB, IR, depth cameras)
  • Background in medical devices, regulated environments, or healthcare applications
  • Experience working in fast-moving early-stage environments
What we offer:
  • Real impact: your code provides care for patients today
  • High autonomy and technical ownership - you'll shape our computer vision architecture
  • Work at the intersection of cutting-edge AI, edge computing, and healthcare
  • A talented, excellent, diverse and international team
  • Cutting-edge stack: embedded AI, robotics, LLMs, multimodal sensing
  • Talented, international team tackling meaningful problems in remote patient monitoring
  • Competitive salary
  • Transparent, mission-driven culture focused on continuous learning