Why This Role?

Tired of ML projects that never leave the notebook? Here, you'll own the full journey: from raw data to a model running in production and serving real users. You'll work on real-world AI problems, learn across the entire ML lifecycle, and grow alongside Data Scientists, Data Engineers, Software Engineers, and Product Managers.

If you're an early- to mid-career ML engineer who wants hands-on impact and quick growth, this is for you.

What You'll Do

Build and improve models

Design, train, and evaluate ML models for real business and technical problems.

Clean and analyze datasets, engineer features, and choose the right algorithms.

Tune and experiment your way to better performance

Take models to production

Build reproducible ML pipelines and workflows.

Deploy models across development, staging, and production environments.

Integrate models into applications through APIs and backend systems

Keep them healthy

Monitor model performance, data quality, and system reliability.

Troubleshoot model, data, and production issues.

Apply solid validation, testing, and version-control practices

Collaborate and grow

Document your experiments, assumptions,



and results so the team can build on them.

Stay on top of emerging ML, deep learning, and Generative AI developments

What You Bring

Bachelor's or Master's in Computer Science, Data Science, AI, Mathematics, Engineering, or a related field

1–4 years of experience in ML, data science, or AI engineering

Strong Python skills

Solid grasp of ML algorithms and statistical concepts

Hands-on experience with Scikit-learn, Pandas, NumPy, and Matplotlib

Experience with supervised and unsupervised learning

Understanding of model evaluation, feature engineering, and hyperparameter tuning

Working knowledge of SQL and databases

Comfort with Git, plus sharp analytical and debugging skills

Bonus Points ✨

PyTorch or TensorFlow experience

Exposure to NLP, computer vision, recommendation systems, or Generative AI

Hands-on work with LLMs, embeddings, RAG, or vector databases

MLflow, Kubeflow, or Airflow

Docker and Kubernetes

AWS, Azure, or GCP

CI/CD and MLOps practices

Deploying models via REST APIs or microservices

Distributed computing with Spark

📌 Machine Learning Engineer (Canada)
🏢 Placement Group
📍 Canada