
ML Application Engineer
- Toronto, ON
- Permanent
- Full-time
- Work in a small team passionate about enabling ML applications throughout the organization.
- Productionize, scale, and productize cutting-edge machine learning solutions.
- Design and develop scalable and robust ML pipelines for predictive data to be consumed by downstream applications to improve the main KPIs, such as member engagement, revenue, and others.
- Design and develop robust processes to monitor production ML pipelines.
- Support production systems to deliver batch and streaming real-time model predictions to all applications.
- Actively participate in solution design and modeling to ensure ML products are developed according to best practices, standards, and ML architectural principles.
- Work closely with our Product, Engineering, and Marketing teams to build the data and ML solutions to address business-critical questions.
- Deploy models and evaluate their performance; constantly test and improve.
- Responsible for model retraining, drift monitoring, pipeline automation, quality control, and governance of production models.
- Work closely with the OPS team to provide the necessary production support.
- 4+ years work experience with ML pipelines and ML-based Python development.
- Strong knowledge of general software engineering principles and practices.
- Expertise with RESTful APIs.
- Experienced building ML- and LLM-based recommendation systems.
- Experience designing and developing back-end components for low-latency and highly-scalable solutions.
- Working knowledge of ML Ops principles and CI/CD.
- Experience managing the machine learning algorithm lifecycle.
- Knowledge of ML-based application design principles.
- Experience with containers and related infrastructures, such as Docker and Kubernetes.
- Familiarity with native AWS tools.
- Strong optimization and debugging skills.
- Self-disciplined, motivated, eager to help, and most importantly, a thirst for continual learning.
- Effective communicator and collaborator, both within the immediate team and across other organizational units.
- Team spirit and a problem-solver mindset.
- Knowledge of data science principles.
- Experience with prompt engineering, retrieval-augmented generation (RAG), and vector databases
- AWS Architect certification.
- Experience with web application development and UI/UX optimization.
- Experience with the design, implementation, and deployment of machine learning algorithms.
- Experience with relational databases and in-memory storage.
- Languages: Python, SQL, Spark, PySpark
- Tools: Snowflake, SnowPark, Splunk
- AWS Services: EKS, SageMaker, Bedrock, DynamoDB, Kinesis, RedShift, Lambda and others
- Containerization: Kubernetes, Docker
- Version Control: GitLab
- Data & Analytics: Dataiku, Tableau
- Recruiter Phone Interview
- Hiring Manager Interview
- Take-home Assessment or remote coding exercise (if applicable)
- Team Interview