Senior Machine Learning Engineer

4w4 weeks ago

CIBC

Toronto, CA · Full-time · C$160,000 – C$220,000

About this role

We’re building a relationship-oriented bank for the modern world. As a Senior Machine Learning Engineer, you will design, build, and productionize scalable machine learning and large language model solutions. These drive our digital-first, customer-centric marketing vision across omni-channel platforms.

You will leverage software engineering expertise to develop robust ML/LLM pipelines, optimize model performance, and ensure seamless integration into production environments. Daily work involves architecting end-to-end ML/LLM Ops workflows with CI/CD, monitoring, and automated retraining. Utilize cloud platforms and big data tools to manage large-scale customer data.

Work closely with AI scientists, cross-functional engineers, and marketing teams to enable intelligent, real-time customer engagement. Collaborate to translate business requirements into technical solutions and drive AI/ML technology adoption. At CIBC, embrace strengths and ambitions to feel empowered and valued.

Apply best practices in software development for high-quality ML systems. Maintain documentation, mentor junior engineers, and contribute to knowledge sharing. Thrive in an optimal work environment with flexible on-site and remote arrangements discussed during interviews.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent experience
5+ years hands-on experience in software development with strong background in architecting, building, and deploying machine learning solutions
Past experience with digital marketing domain highly preferred
Strong software engineering skills including git, unit testing, code reviews, and containerization
Experience with cloud platforms such as Databricks, GCP, AWS
Proficiency with big data tools like Spark for processing large-scale data
Knowledge of ML/LLM Ops including CI/CD, monitoring, and model governance

Responsibilities

Build, optimize, and productionize machine learning and large language model pipelines for marketing applications
Architect and manage end-to-end ML/LLM Ops workflows, including CI/CD, automated model deployment, monitoring, retraining, and governance
Apply best practices in software development like version control, testing, code reviews, and modular design
Utilize cloud platforms (Databricks, GCP, AWS) and big data tools (Spark) to process large-scale customer data
Implement tools and frameworks for continuous monitoring, performance evaluation, and automated retraining of ML/LLM models
Work cross-functionally with AI scientists, engineers, and marketing stakeholders to translate requirements into solutions
Maintain clear documentation of ML/LLM pipelines and mentor junior engineers