
Machine Learning Developer - AI/ML
- Montréal, QC
- Permanent
- Temps-plein
- Govern and contribute to the industrialization of ML/AI projects, focusing on the deployment and maintenance of pipelines and models in production.
- Contribute to the identification of new or improved approaches and technical tools.
- Acting as a consultant to guide technologies and/or advising on proofs of concept for forecasting/prescribing and AI projects.
- Design and implement services, tests, and interfaces that support the deployment of ML projects.
- Ensure the security, robustness, and performance of ML solutions during production deployments.
- Design and implement scalable, continuous, and automated processes for deploying, monitoring, and versioning ML models.
- Guide, collaborate, and support users in optimizing ML pipelines, by becoming an expert on the Databricks platform.
- Mentor and engage ML/AI practitioners at Ubisoft, sharing knowledge and best practices in ML Ops.
- Participate in proof-of-concept projects, advancing knowledge of new ML technologies.
- 3 to 5 years of complex experience and in-depth expertise related to the position.
- Team player and excellent organizational, interpersonal and communication skills.
- Passion for leveraging data science in problem solving.
- Ability to assess problems quickly, both qualitatively and quantitatively.
- Ability to navigate between the big picture and the details of Data Science / ML Engineering, with a strong programming background with Java, Scala or Python.
- Experience in Machine Learning, Data Science, or a related field, with a focus on improving model and data quality, and the proven ability to build automated AI processes and manage large-scale data pipelines.
- Experience in deploying models, setting up model validation, and operationalizing AI/ML systems at scale in production environments.
- Experience with ML concepts, tools such as MLFlow, and frameworks/libraries like Scikit-learn, PyTorch, XGBoost. Experience with Databricks is an asset.
- Knowledge of Big Data-Spark, PySpark, data engineering, and pipeline architecture technologies is essential.
- Have a foundation in software engineering principles and proficiency in DevOps tools and practices
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or Software or equivalent.