Machine Learning Developer - Développeur en apprentissage automatique

Grizzlytrek Group•Montreal, QC

46d•Hybrid

About The Position

GrizzlyTrek is seeking one Machine Learning Developer to join our value-added client's team at their Montreal, Quebec location. This is a full-time contract position with potential for extension and conversion to a permanent role. You will design, build, deploy, and maintain end-to-end ML solutions that power data-driven decision-making across a digital ecosystem. Working closely with cross-functional teams, including data engineers, software developers, product owners, and project leaders, you will transform real-world data and business problems into scalable, resilient, and high-impact ML systems.

Requirements

5+ years of experience designing and implementing end-to-end ML solutions in production
Strong command of ML algorithms, model development, training, validation, and optimization
Expertise in Python, ML libraries, and version control (Git)
Clear understanding of model evaluation, data leakage, and the bias/variance trade-off
Hands-on experience with cloud platforms (AWS/Azure/GCP) and MLOps practices, including Docker, CI/CD, deployment, and monitoring
Demonstrated success deploying and maintaining production ML models and writing modular, production-grade code
Strong experience preparing, transforming, and validating complex real-world datasets (Snowflake or similar cloud data platforms)
Excellent problem-solving abilities and communication skills
Proven ability to work cross-functionally with engineering and product teams

Nice To Haves

Experience with enterprise system data (SAP, Salesforce, PLM, Teamcenter)
Familiarity with LLMs/SLMs and modern ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace)
Experience with Agile methodologies
Knowledge of SQL and cloud data warehousing

Responsibilities

Build and own ML solutions from data ingestion through modelling, evaluation, deployment, and monitoring
Develop, train, and evaluate machine learning models using modern ML frameworks and libraries
Deploy, operationalize, and maintain ML models in production environments, implementing CI/CD pipelines, Docker/containerization, orchestration, automated retraining, and monitoring
Write modular, production-ready Python code and reusable ML components
Extract, clean, transform, and validate datasets from diverse sources to support robust model development
Handle ambiguity in real-world, imperfect data and design reproducible data processing pipelines
Apply rigorous evaluation practices: cross-validation, bias/variance analysis, overfitting detection, and data leakage prevention
Monitor models for drift, performance degradation, and operational issues
Collaborate cross-functionally with engineers, developers, architects, and project teams to align technical solutions with business objectives
Communicate findings, risks, solution design, and technical trade-offs to both technical and non-technical stakeholders
Work with emerging approaches such as LLMs, SLMs, embeddings, and prompt-based workflows