Machine Learning Engineer

Take Your Career to New Heights with Flair Airlines

We’re here to make air travel affordable for everyone, and we’re looking for talented individuals who want to help us continue changing the industry for the better. As Canada’s most reliable airline, we're all about offering real value. As we continue to expand our network, increase flight frequencies, and introduce more services, we’re looking for passionate team members to help us in our mission to make air travel accessible for everyone. We know who we are, and we’re confident in our approach.

Your Team:

Join the Digital - Engineering team at Flair as a Machine Learning Engineer, where you will play a key role in designing, building, deploying, and maintaining production machine learning systems. Reporting to the Director, Software Engineering, you will work closely with Data Engineers, Software Engineers, and business stakeholders to transform data and predictive models into reliable, scalable solutions that support business needs. This is a hands‑on role focused on developing production‑ready machine learning solutions, improving model performance and reliability, and ensuring machine learning systems operate effectively at scale. You will also have the opportunity to contribute to the ongoing development and evolution of Flair’s technology platform.

This is a full‑time, on‑site position that can be based at any of our Edmonton (YEG), Calgary (YYC), Vancouver (YVR), or Toronto (YYZ) offices.

A Day in the Life:

As a Machine Learning Engineer, you will:

• Design, develop, and deploy machine learning models for classification, prediction, recommendation, anomaly detection, forecasting, and optimization.
• Build end-to-end machine learning pipelines, including data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
• Develop production‑quality Python code using machine learning frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow.
• Deploy machine learning models as APIs, batch‑processing jobs, or real‑time inference services.
• Build and maintain cloud‑based machine learning infrastructure using platforms such as AWS, Azure, or GCP.
• Work with large datasets using SQL, data warehouses, and distributed processing frameworks.
• Implement MLOps practices, including experiment tracking, model versioning, CI/CD, automated retraining, and model monitoring.
• Monitor model performance, data quality, model drift, latency, and reliability in production environments.
• Collaborate with DevOps Engineers to productionize experimental models and transform prototypes into reliable, scalable systems.
• Partner with Data Engineers to develop reliable datasets and pipelines for model training and inference.
• Evaluate emerging machine learning and generative AI technologies, including large language models (LLMs), embeddings, vector databases, and retrieval‑augmented generation (RAG), where appropriate.
• Communicate model performance, limitations, and business impact clearly to both technical and non‑technical stakeholders.
• Perform additional duties as required to support business objectives.

What You Bring to the Role:

The preferred candidate should exemplify Flair's core values of respect, honesty, accountability, and efficiency while demonstrating success in previous roles. Additionally, we are seeking individuals who possess:

• Education:
  • Bachelor’s or master’s degree in computer science, Data Science, Statistics, Engineering, or a related field
• Experience:
  • Minimum of three (3) years of experience in machine learning, data science, software engineering, or a related technical field, with hands‑on experience developing and deploying machine learning solutions.
  • Experience developing machine learning solutions using libraries such as scikit‑learn, XGBoost, PyTorch, or TensorFlow.
  • Experience deploying applications or machine learning models in a cloud environment.
  • Experience working with SQL and large datasets.
  • Experience with Docker, Git, CI/CD, APIs, and software engineering practices.
  • Experience with AWS services such as SageMaker, S3, Lambda, ECS/EKS, Glue, and CloudWatch is an asset.
  • Familiarity with MLOps tools such as MLflow, SageMaker Pipelines, Airflow, Kubeflow, or similar platforms is an asset.
• Skills and Abilities:
  • Strong programming skills in Python.
  • Strong knowledge of SQL and data processing.
  • Strong understanding of core machine learning concepts, including supervised and unsupervised learning, feature engineering, model evaluation, overfitting, cross-validation, and hyperparameter tuning.
  • Strong analytical and problem‑solving skills.
  • Ability to develop reliable, product