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Senior Machine Learning Engineer Jobs in Quebec (NOW HIRING)

En tant que Senior Cloud Data Engineer chez Progi, vous serez responsable de la transformation et ... Machine Learning / AI en production . Vous agirez en propriétaire end-to-end des pipelines de ...

We are seeking a senior distributed machine learning (ML) research developer to join our team working on a novel AI safety agenda. In this role, you will work closely with ML research scientists to ...

As a Senior II Applied Scientist on Coveo's Knowledge AI team, you will help build the language ... Collaborate closely with machine learning developers and the machine learning platform team to ...

As a Senior II Applied Scientist on Coveo's Knowledge AI team, you will help build the language ... Collaborate closely with machine learning developers and the machine learning platform team to ...

Apply Early

As a Senior II Applied Scientist on Coveo's Knowledge AI team, you will help build the language ... Collaborate closely with machine learning developers and the machine learning platform team to ...

Apply Early

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 ...

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Senior Machine Learning Engineer information

What are some common challenges Senior Machine Learning Engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What does a Senior Machine Learning Engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

What is the difference between Senior Machine Learning Engineer vs Data Scientist?

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What are the most commonly searched types of Machine Learning Engineer jobs in Quebec? The most popular types of Machine Learning Engineer jobs in Quebec are:
What are popular job titles related to Senior Machine Learning Engineer jobs in Quebec? For Senior Machine Learning Engineer jobs in Quebec, the most frequently searched job titles are:
What cities in Quebec are hiring for Senior Machine Learning Engineer jobs? Cities in Quebec with the most Senior Machine Learning Engineer job openings:
Infographic showing various Senior Machine Learning Engineer job openings in Quebec as of June 2026, with employment types broken down into 96% Full Time, 2% Part Time, and 2% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution.

Senior Cloud Data Engineer

Progi

Trois-rivieres, QC • On-site

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

En tant que Senior Cloud Data Engineer chez Progi, vous serez responsable de la transformation et orchestration de la plateforme de données cloud , avec un focus particulier sur les couches Silver et Gold (BigQuery, Dataform, Cloud Composer) .

Votre rôle sera central dans l’évolution de notre plateforme vers un modèle data-driven avancé , incluant l’optimisation des coûts, la performance des pipelines et la mise en place des premières capacités en Machine Learning / AI en production .

Vous agirez en propriétaire end-to-end des pipelines de données (architecture medallion) et contribuerez activement à bâtir une plateforme robuste, performante et prête pour les usages analytiques et prédictifs.
Responsabilités principales

Ownership de la plateforme data (Medallion)

  • Concevoir, construire et maintenir les couches Silver et Gold dans BigQuery
  • Assurer l’implémentation complète d’une architecture Medallion (bronze / silver / gold)
  • Garantir la qualité, la cohérence et la scalabilité des datasets
Orchestration & pipelines
  • Développer et maintenir des pipelines ELT/ETL avec Dataform et Cloud Composer
  • Orchestrer les workflows de transformation et de chargement de données
  • Assurer un monitoring fiable des pipelines en production
Ingestion de données
  • Implémenter des stratégies d’ingestion via :
    • APIs
    • Bases de données (MySQL, etc.)
  • Standardiser les flux d’ingestion pour assurer leur robustesse et leur réutilisabilité
Optimisation BigQuery (clé du rôle)
  • Optimiser les coûts et les performances des workloads BigQuery
  • Mettre en place des bonnes pratiques :
    • partitioning / clustering
    • query optimization
    • data lifecycle management
  • Suivre et améliorer en continu l’efficacité de la plateforme
Data & Machine Learning enablement
  • Collaborer à la mise en place des premiers cas d’usage ML/AI
  • Préparer les données pour des pipelines de machine learning en production
  • Participer à l’intégration avec les outils GCP liés au ML (ex : Vertex AI)
CI/CD & bonnes pratiques
  • Contribuer à la mise en place et l’amélioration des pipelines CI/CD (Git, automatisation)
  • Appliquer les standards d’ingénierie modernes (tests, versioning, documentation)
Collaboration
  • Travailler étroitement avec les équipes BI, produit et techniques
  • Participer à la définition des besoins analytiques et des modèles de données
  • Promouvoir une culture de data engineering orientée performance et valeur métier
Profil recherché Expérience
  • Minimum 5 ans d’expérience en data engineering
  • Minimum 2 ans sur Google Cloud Platform (GCP)
Compétences techniques
  • Excellente maîtrise de :
    • SQL (avancé)
    • Python
  • Expérience solide avec :
    • BigQuery
    • Dataform
    • Cloud Composer
  • Bonne compréhension des architectures :
    • Data warehouse
    • Medallion architecture
Expérience pratique souhaitée
  • Construction de pipelines de données en production
  • Optimisation des coûts et performance BigQuery
  • Migration ou modernisation vers le cloud
  • Expérience avec des projets de Machine Learning (atout fort)
Atouts
  • Expérience avec CI/CD et Git
  • Connaissance des outils d’ingestion (API, bases transactionnelles)
  • Expérience dans un environnement data à forte volumétrie
Ce qui différencie ce rôle
  • Ownership complet de la couche de transformation (pas juste builder)
  • Impact direct sur :
    • les coûts cloud
    • la performance data
    • la stratégie AI/ML
  • Rôle hybride Data Engineer + Data Platform + AI en devenir
La connaissance de l'anglais est essentiel afin d'accomplir sa prescription de travail avec les différentes parties prenantes.
  • Excellente maîtrise de :
    • SQL (avancé)
    • Python
  • Expérience solide avec :
    • BigQuery
    • Dataform
    • Cloud Composer
  • Bonne compréhension des architectures :
    • Data warehouse
    • Medallion architecture
  • Expérience pratique souhaitée
    • Construction de pipelines de données en production
    • Optimisation des coûts et performance BigQuery
    • Migration ou modernisation vers le cloud
    • Expérience avec des projets de Machine Learning (atout fort)