Machine Learning Engineer (Canada)
Tiger Analytics Inc. Open · verified Oct 22, 2026 At a glance
Mid-level Machine Learning Engineering role at Tiger Analytics Inc. Toronto · full-time · no salary stated · we estimate CA$133,650–CA$197,920.
Tiger Analytics Inc. has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build high-performance machine learning systems and reusable data pipelines for models moving into production
- Put deployment, execution, validation and monitoring in place for data science solutions, then improve them
- Package production code and reusable libraries in containers so they can be installed and deployed
- Keep production ML systems scalable and troubleshoot model problems, including retraining and validation needs
What you bring
- A computer science degree or related qualification, plus at least five years of work experience
- You can build data and model pipelines with data engineers and scientists, and support ML tests and experiments
- You have hands-on experience with Python, Spark, Hadoop and Docker, plus good coding practices in continuous integration
- You have worked on big data projects using structured and unstructured data, and understand production ML systems end to end
Who this fits
This suits a mid-level machine learning engineer who can work across pipelines, infrastructure and production model support. The role is onsite in Toronto and involves collaborating with remote teams.
From the employer
Tiger Analytics is an advanced analytics consulting firm. We are the trusted analytics partner for several Fortune 100 companies, enabling them to generate business value from data. Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
We are looking for a motivated and passionate Machine Learning Engineers for our team.
As part of this job, you will be responsible for:
- Providing solutions for the deployment, execution, validation, monitoring, and improvement of data science solutions
- Creating Scalable Machine Learning systems that are highly performant
- Building reusable production data pipelines for implemented machine learning models
- Writing production-quality code and libraries that can be packaged as containers, installed and deployed
Requirements
- Bachelor's degree or higher in computer science or related, with 5+ years of work experience
- Ability to collaborate with Data Engineers and Data Scientist to build data and model pipelines and help running machine learning tests and experiments
- Ability to manage the infrastructure and data pipelines needed to bring ML solution to production
- End-to-end understanding of applications being created and maintain scalable machine learning solutions in production
- Ability to abstract complexity of production for machine learning using containers
- Ability to troubleshoot production machine learning model issues, including recommendations for retrain, revalidate, and improvements
- Experience with Big Data Projects using multiple types of structured and unstructured data
- Ability to work with a global team, playing a key role in communicating problem context to the remote teams
- Excellent communication and teamwork skills
Additional Skills Required:
- Python, Spark, Hadoop, Docker, with an emphasis on good coding practices in a continuous integration context, model evaluation, and experimental design
- Test-driven development (prefer py. test/nose), experience with Cloud environments
- Proficiency in statistical tools, relational databases, and expertise in programming language like python/SQL is desired.
Good to have:
- Knowledge of ML frameworks like Scikitlearn, Tensorflow, Keras, etc.
- Knowledge of MLflow, Airflow, Kubernetes
- Knowledge on any of the cloud-native MLaaS offerings like AWS SageMaker, AzureML, or Google AI platform
Benefits
Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.