Machine Learning Developer

Job Title: Machine Learning Developer

Job Location: Montreal, QC

Job Type: Contract

Job Overview:

• We are seeking an experienced Machine Learning Developer to design, build, deploy, and maintain end to end ML solutions that power data driven decision making across our digital ecosystem.
• This role is ideal for someone who thrives at the intersection of applied machine learning, ML Ops engineering, and production-grade software development.
• The candidate will work closely with cross functional teams including data engineers, software developers, product owners, and project leaders to transform ambiguous real world data and business problems into scalable, resilient, and high impact ML systems.

Responsibilities:

End To End Machine Learning Development:

• 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.

Production Engineering and MLOps:

• 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.

Data Preparation and Feature Engineering:

• 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.

Model Quality and Risk Management:

• Apply rigorous evaluation practices: cross validation, bias/variance analysis, overfitting detection, and data leakage prevention.
• Monitor models for drift, performance degradation, and operational issues.

Collaboration and Stakeholder Engagement:

• Work cross functionally with engineers, developers, architects, and project teams to align technical solutions with business objectives.
• Clearly communicate findings, risks, solution design, and technical trade offs to both technical and non technical stakeholders.

Innovation and Modern ML:

• Work with emerging approaches such as LLMs, SLMs, embeddings, and prompt based workflows.
• Stay up to date with current ML engineering, MLOps practices, tooling, and cloud native capabilities.

Required Qualifications, Experience and Skills:

• 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/Google Cloud Platform) 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 (in Snowflake or similar cloud data platforms).
• Experience with enterprise system data (SAP, Salesforce, PLM, Teamcenter) is desirable.
• Familiarity with LLMs/SLMs and modern ML frameworks (e.g., PyTorch, TensorFlow, HuggingFace).
• Excellent problem solving abilities and communication skills.
• Proven ability to work cross functionally with engineering and product teams.

A Snapshot Of A Typical Day:

• Reviewing model performance dashboards to detect drift or anomalies.
• Working with engineers to refine a data pipeline or debug a production model issue.
• Pair programming with developers to implement new pipeline components or optimize code for production.
• Running experiments on new ML architectures or tuning hyperparameters for an active use case.
• Meeting with project teams to translate business needs into ML ready requirements, and effectively communicate the solution design to build confidence, validate outcomes and drive adoption.
• Evaluating risks such as data leakage, insufficient sampling, data imbalance, or other data quality issues and proposing mitigations.
• Exploring and testing improvements using LLM based workflows or modern ML tooling.

Additional Information:

• This role offers the opportunity to make meaningful impact by delivering scalable, stable, and business critical intelligent systems.