Junior Machine Learning Engineer

BMO•Toronto, ON

16h•Hybrid

About The Position

Researches, builds, and implements scalable artificial intelligence systems capable of learning and making predictions to business requirements. Enhances data pipelines and lakes to ensure data is clean, accurate, and optimized for machine learning models. Monitors, evaluates, and optimizes learning processes to continuously improve high-performance models. Works with other data and analytics professionals to optimize, refine, automate and scale analysis into repeatable analytics solutions and decision support tools. Designs and develops machine learning (ML) and deep learning systems. Runs machine learning tests and experiments. Trains and retrains systems to prevent drift and optimize results. Solves complex problems with multi-layered data sets, extends existing ML frameworks and optimizes existing machine learning libraries. Develops Machine Learning apps, implements algorithms, and builds tools to apply ML frameworks. Turns unstructured data into useful information by auto-tagging images and text-to-speech conversions. Develops ML algorithms to analyze huge volumes of historical data to make predictions. Runs tests, performs statistical analysis, and interprets test results. Focus is primarily on business/group within BMO; may have broader, enterprise-wide focus. Exercises judgment to identify, diagnose, and solve problems within given rules. Works independently on a range of complex tasks, which may include unique situations. Broader work or accountabilities may be assigned as needed. Take measured risks while protecting the bank by applying our Risk Management Framework in the execution of your role, in line with our Risk Culture and within our approved Risk Appetite, making sound and risk informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents), laws and regulations.

Requirements

MUST HAVE experience building ML models using cloud-based tech stack
Must be proficient with Python and TensorFlow
Systems Thinking
Mathematics, Statistics & Operations Research
Critical thinking
Creative reasoning
Computational Thinking and Programming
Deep Learning
Machine Learning
Scaling Models
Continuous Integration and Continuous Delivery/Deployment
ML algorithm
Verbal & written communication skills
Collaboration & team skills
Analytical and problem solving skills
Data driven decision making
Typically between 2-4 years of relevant experience
Post-secondary degree in related field of study or an equivalent combination of education and experience
Technical proficiency gained through education and/or business experience

Responsibilities

Researches, builds, and implements scalable artificial intelligence systems capable of learning and making predictions to business requirements.
Enhances data pipelines and lakes to ensure data is clean, accurate, and optimized for machine learning models.
Monitors, evaluates, and optimizes learning processes to continuously improve high-performance models.
Works with other data and analytics professionals to optimize, refine, automate and scale analysis into repeatable analytics solutions and decision support tools.
Designs and develops machine learning (ML) and deep learning systems.
Runs machine learning tests and experiments.
Trains and retrains systems to prevent drift and optimize results.
Solves complex problems with multi-layered data sets, extends existing ML frameworks and optimizes existing machine learning libraries.
Develops Machine Learning apps, implements algorithms, and builds tools to apply ML frameworks.
Turns unstructured data into useful information by auto-tagging images and text-to-speech conversions.
Develops ML algorithms to analyze huge volumes of historical data to make predictions.
Runs tests, performs statistical analysis, and interprets test results.