Machine Learning Engineer

Datatonic•Montreal, QC

20d•Remote

About The Position

At Datatonic, a premier Google Cloud partner in AI, the Senior Machine Learning Engineer will drive transformation for world-class businesses. This role involves pushing the boundaries of technology with expertise in machine learning, data engineering, and analytics on Google Cloud Platform. The successful candidate will engineer high-quality Python code for production software and rapid prototypes, advocate for best practices, and potentially lead projects and client discussions. Responsibilities include building trusted relationships, finding creative machine learning solutions, scoping projects, and overseeing their delivery. Strong ML & Data Science fundamentals, comfort with model optimization and deployment tools, and excellent communication and consulting skills are essential for meeting business needs and delivering innovative AI & Cloud solutions.

Requirements

Multiple years experience as a Machine Learning Engineer, preferably with a consulting background.
Proficiency in Python as a backend language, capable of delivering production-ready code in well-tested CI/CD pipelines.
Familiarity with cloud platforms such as Google Cloud, AWS, or Azure.
Hands-on experience with foundational software engineering practices.
Strong knowledge of SQL for querying and managing data.
Experience scaling computations using GPUs or distributed computing systems.
Familiarity with exposing machine learning components through web services or wrappers (e.g., Flask in Python).
Strong communication and presentation skills to effectively convey technical concepts.

Nice To Haves

Scale-up experience.
Cloud certifications (Google Cloud Professional Machine Learning Engineer, AWS Solution Architect, etc.).

Responsibilities

Translating Requirements: Interpret vague requirements and develop models to solve real-world problems.
Data Science: Conduct ML experiments using programming languages with machine learning libraries.
GenAI: Leverage generative AI to develop innovative solutions.
Optimisation: Optimise machine learning solutions for performance and scalability.
Custom Code: Implement tailored machine learning code to meet specific needs.
Data Engineering: Ensure efficient data flow between databases and backend systems.
MLOps: Automate ML workflows, focusing on testing, reproducibility, and feature/metadata storage.
ML Architecture Design: Create machine learning architectures using Google Cloud tools and services.
Engineering Software for Production: Build and deploy production-grade software for machine learning and data-driven solutions.