Mid-Level ML Engineer: Cloud, MLOps & Deployment

Aviva; Tampere; Hybrid; EUR 79,000 - 101,000; Full time

Benefits offered by this job

Health benefits

Hybrid work model

Career development opportunities

Retirement savings

Job summary

Aviva Canada is seeking an Intermediate Machine Learning Engineer to join the AI/ML Operations & Platform team. You will design and deploy ML pipelines, collaborate with data scientists and engineers, and ensure compliant and scalable ML services across cloud and on-premise environments.

The role emphasizes MLOps, model deployment, monitoring, and collaboration with cross-functional teams, with a hybrid work arrangement and a strong focus on security and data governance.

Qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, or related field.
• 2-4 years of experience in machine learning engineering or related roles.
• Proficiency in Python and data processing/ML libraries.

Responsibilities

• Assist in designing and implementing ML pipelines and services in cloud and on-premise environments.
• Collaborate with cross-functional teams to gather requirements and contribute to scalable ML solutions.
• Support model deployment, monitoring, and performance optimization.
• Apply MLOps practices such as CI/CD, model versioning, and retraining workflows.
• Work with Snowflake and AWS services to support ML model deployment.
• Contribute to the development and maintenance of on-premise ML workflows.
• Ensure adherence to data privacy, security, and compliance standards.
• Participate in code reviews and knowledge sharing within the team.

Skills

Python

Data processing

Analytical skills

Problem solving

Collaboration

Education

Bachelor's degree

Tools

AWS

Snowflake

Linux

Jenkins

Docker

SQL

MLflow

Airflow

Job description

Aviva Canada is seeking an Intermediate Machine Learning Engineer to join the AI/ML Operations & Platform team. You will design and deploy ML pipelines, collaborate with data scientists and engineers, and ensure compliant and scalable ML services across cloud and on-premise environments.

The role emphasizes MLOps, model deployment, monitoring, and collaboration with cross-functional teams, with a hybrid work arrangement and a strong focus on security and data governance.