Mid-Level ML Engineer - Hybrid, Impact & Growth

Aviva plc; Markham; Hybrid; CAD 90,000 - 115,000; Full time

Benefits offered by this job

Hybrid work model

Annual bonus eligibility

Retirement savings

Share plan

Health benefits

Personal wellness

Volunteer opportunities

Career development opportunities

Job summary

Aviva Canada is seeking an ML Engineer to design and implement ML pipelines across cloud and on-prem environments. You will collaborate with cross-functional teams, support deployment, monitoring, and optimize performance, while applying MLOps practices such as CI/CD and retraining workflows.

The role requires 2–4 years of ML engineering experience, strong Python and ML libraries, and familiarity with AWS/Snowflake and Linux. Hybrid work model and ongoing professional development are offered.

Qualifications

• Bachelor’s degree in CS, data science, engineering, or related field.
• 2–4 years of ML engineering or related experience.
• Proficiency in Python and ML libraries (Pandas, NumPy, scikit-learn, XGBoost).
• Experience with AWS and Snowflake; Linux familiarity.
• Basic experience with Jenkins and Docker.

Responsibilities

• Design and implement ML pipelines and services in cloud and on-prem environments.
• Collaborate with cross-functional teams to gather requirements for scalable ML solutions.
• Support model deployment, monitoring, and performance optimization.
• Apply MLOps practices including CI/CD, model versioning, retraining workflows.
• Work with Snowflake and AWS services to support ML model deployment.
• Maintain on-premise ML workflows and ensure data privacy and security standards.
• Participate in code reviews and knowledge sharing.

Skills

Python

Pandas

NumPy

scikit-learn

XGBoost

AWS

Snowflake

Azure

GCP

Linux

Jenkins

Docker

SQL

MLflow

Airflow

Spark

Snowpark

Education

Bachelor’s degree in CS, Data Science, Engineering, or related field

Tools

Jenkins

Docker

MLflow

Airflow

Job description

Aviva Canada is seeking an ML Engineer to design and implement ML pipelines across cloud and on-prem environments. You will collaborate with cross-functional teams, support deployment, monitoring, and optimize performance, while applying MLOps practices such as CI/CD and retraining workflows.

The role requires 2–4 years of ML engineering experience, strong Python and ML libraries, and familiarity with AWS/Snowflake and Linux. Hybrid work model and ongoing professional development are offered.