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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing, training, and evaluating AI/ML models, with a strong focus on production deployment, model lifecycle management, and collaboration across multidisciplinary teams. Proficient in data governance, responsible AI practices, and effective communication of technical concepts to diverse audiences.
Highest-signal resume keywords
AI/ML Model DesignProduction Deployment RequirementsProficiency in PyTorch and TensorFlowBilingual Proficiency in French and EnglishExperience with AWS SageMaker and Azure ML
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/ML Model EvaluationDataset and Ground-Truth CreationBias Analysis and Robustness EvaluationModel Fine-TuningExperiment TrackingVersion Control with GitModel CI/CDGenerative AI ApproachesData Quality ConsiderationsTechnical Documentation
Soft Skills
CollaborationClear CommunicationMethodological RigorProblem-SolvingInterdisciplinary Teamwork
Tools & Technologies
AWS SageMakerAWS BedrockAzure MLPyTorchTensorFlowGitMLOps ToolsExperiment Tracking ToolsTraining PlatformsMonitoring Tools
Industry Keywords
Applied MathematicsData ScienceEngineeringResponsible AIData GovernanceTelecommunicationsNetworkingGenerative SystemsModel LifecycleRegulatory Compliance
Tech Stack
Tools & technologiesAWSAzurePyTorchTensorflow
About the role
Key responsibilities & impact- Translate a product or operational need into a measurable AI/ML problem, with hypotheses, success criteria, and usage limitations.
- Define or contribute to model architecture, AI/ML approaches, and the technical choices required for the solution.
- Prepare, select, or specify the datasets, metrics, ground truth, and evaluation strategies required for training and validation.
- Train, fine-tune, improve, and evaluate models by analyzing their performance, biases, limitations, and conditions of use.
- Assess the scalability and long-term sustainability of the solution in light of future product volumes, use cases, data, model versions, and constraints.
- Define production deployment conditions: acceptance thresholds, success metrics, fallback and rollback procedures, and human responsibilities when confidence is insufficient.
- Document assumptions, results, and constraints, and contribute with Software and Platform teams to post-deployment monitoring of performance, drift, quality, incidents, and continuous improvement.
- Collaborate with Product, Data, Software, and Platform/MLOps teams.
Requirements
What you’ll need- University degree in applied mathematics, data science, engineering, or a related quantitative field, with a master's or PhD in AI, or equivalent experience.
- Significant experience (5+ years) designing, training, and evaluating AI/ML models for real-world product or operational problems, including taking solutions from prototype to production use.
- Proficiency with modeling languages, libraries, and frameworks (PyTorch, TensorFlow, ...), version control tools (Git), and experiment tracking tools.
- Strong methodological rigor in evaluation, including dataset and ground-truth creation, metric selection, success thresholds, and analysis of bias, robustness, and usage limitations.
- Understanding of production deployment requirements and the model lifecycle, including acceptance thresholds, fallback and rollback procedures, human oversight, and performance and drift monitoring.
- Demonstrated ability to clearly document assumptions, results, constraints, and dependencies, and to explain model limitations to product and non-technical audiences.
- Ability to work closely with Product, Data (Data Scientists, Data Stewards, and Data Operations), Software, and Platform/MLOps teams in a multidisciplinary environment.
- Bilingual proficiency in French and English, both spoken and written.
- Experience deploying models to production and collaborating with MLOps or Platform teams on model CI/CD, version management, monitoring, and retraining.
- Experience with training and inference platforms (e.g., AWS SageMaker, AWS Bedrock, Azure ML), as well as LLM and generative AI approaches (fine-tuning, RAG, and evaluation of generative systems).
- Knowledge of data governance and data quality considerations, as well as ground-truth creation and annotation practices.
- Commitment to responsible AI, including explainability, fairness, privacy, security, and regulatory compliance for deployed models.
- Previous experience in a technology or industrial product environment (telecommunications, networking, or a related sector).
- Recognized scientific or technical contributions, or active engagement with the state of the art.
Benefits
Comp & perks- EXFO is an equal opportunity employer
- Inclusive and diverse work environment