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Bell

Dev. Machine Learning Engineering II

Toronto, ON, Canada

Hybrid

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Job details

Workplace: Hybrid
Apply by: Oct 22, 2026
Experience: 3+ years of experience in a machine learning engineering or related role.
Posting language: English
Posted: Oct 6, 2026

Original job description

Req Id: 433020

Connection is everything. It drives us to innovate, explore, and stay close to what matters to us most. At Bell, we’re building a more connected future through world-class networks, AI-powered solutions, and digital experiences that elevate how people live, work, and play every day.

We believe in empowering people. That’s why we equip our teams with cutting-edge technology, AI tools, and a collaborative environment that supports creativity and growth. Want to be part of a diverse team where your work makes a real impact? If you’re inspired by innovation that advances how people connect and transforms what’s possible, you belong on #TeamBell.

At Bell, data science experts like you are shaping the future. You'll create, apply and work with algorithms that learn and analyze data, just like the human brain. By leveraging AI and machine learning technologies, you'll help us deliver better experiences and service innovations to Canadians.

Plus, through our partnerships, you'll have the opportunity to learn and grow alongside other AI professionals to advance your career and make a lasting impact.

Summary

We are seeking a highly skilled and experienced Machine Learning Engineering Developer II to join our team. This role requires a strong understanding of machine learning principles, software engineering best practices, and cloud infrastructure. The ideal candidate will be responsible for the development, deployment, and maintenance of ML models and pipelines in a production environment.

Key Responsibilities

Develop, deploy, and maintain machine learning models in production on cloud platforms (specify preferred platforms, e.g., AWS, Azure, GCP).
Design, implement, and maintain infrastructure-as-code (IaC) for ML applications, ensuring scalability, reliability, and maintainability.
Develop, enhance, and maintain CI/CD pipelines for ML model development and deployment.
Build, automate, and maintain machine learning dependencies and artifact pipelines, ensuring efficient and reproducible workflows.
Collaborate closely with product teams to accelerate development cycles, improve product robustness, and address production issues.
Proactively identify and resolve performance bottlenecks and other issues in ML pipelines and applications.
Contribute to the development and improvement of ML engineering best practices and standards.
Mentor and guide junior engineers.
Proactive Problem-Solving: Identifies critical needs and takes decisive action to address them effectively and efficiently.
Innovation and Continuous Improvement: Actively seeks and implements new ideas and innovative solutions, proactively exploring new areas and opportunities for growth.
Customer-Centric Approach: Demonstrates a deep understanding of customer needs, anticipating and exceeding expectations to drive customer satisfaction.
Analytical and Solution-Oriented: Masterfully applies systematic problem-solving methodologies to complex challenges, consistently delivering effective solutions.
Learning Agility and Continuous Development: Proactively investigates mistakes, fosters a culture of learning from experience, and actively seeks opportunities to expand skills and responsibilities.
Mentorship and Collaboration: Mentors and guides junior team members, fostering a collaborative environment of knowledge sharing and mutual support.

Critical Qualifications

Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field. Relevant experience may be considered in lieu of a degree.
3+ years of experience in a machine learning engineering or related role.
Cloud Associate Cloud Engineer certification (e.g., AWS Certified Cloud Practitioner, Azure Fundamentals, Google Cloud Certified Professional Cloud Architect)
Proficiency in SQL.
Understanding of big data processing and parallel processing techniques like Hadoop, Kafka, Apache etc.
Experience with containerization technologies (e.g., Docker, Kubernetes).
Strong knowledge of cloud computing concepts and services.
Experience with CI/CD and DevOps practices.
Proficiency in software development lifecycle (SDLC) and machine learning lifecycle (MLLC) methodologies.
Strong programming skills in Python or a similar language.
Experience with version control systems (e.g., Git).
Excellent problem-solving, analytical, and communication skills.
Ability to work independently and as part of a team.

Preferred Qualifications

Experience with specific cloud platforms (AWS, Azure, GCP)
Experience with specific orchestration tools (e.g., Airflow, Argo).
Experience with monitoring and logging tools (e.g., Prometheus, Grafana).
Contributions to open-source projects.

Adequate knowledge of French is required for positions in Quebec.

Additional Information:

Position Type: Management Job Status: Regular - Full Time Job Location: Canada : Ontario : Toronto Work Arrangement: Hybrid Application Deadline: 10/21/2026

For work arrangements that are ‘Hybrid’, successful candidates must be based in Canada and report to a set Bell office for a minimum of 3 days a week. Recognizing the importance of work-life balance, Bell offers flexibility in work hours based on the business needs.

Please apply directly online to be considered for this role. Applications through email will not be accepted.

We know that caring for our team members is at the heart of a healthy, positive and thriving workplace. As part of our team, you’ll enjoy a comprehensive compensation package that includes a competitive salary and a wide range of benefits to support the well-being of you and your family. As soon as you join us, you'll be eligible for medical, dental, vision and mental health benefits that you can tailor to your specific needs. Plus, as a Bell team member, you'll enjoy a 35% discount on our services and access exclusive offers from our partners.

At Bell, we are proud of our focus on fostering an inclusive and accessible workplace where all team members feel valued, respected, supported, and that they belong.

Bell is committed to clarity in our hiring process. All roles posted are opportunities we’re actively recruiting for, unless stated otherwise. We also want to make sure that everyone has an equal opportunity to join our team. We encourage individuals who may require accommodations during the hiring process to let us know. For a confidential inquiry, email your recruiter or recruitment@bell.ca to make arrangements. If you have questions or feedback regarding accessibility at Bell, we invite you to complete the Accessibility feedback form or visit our Accessibility page for other ways to contact us.

Artificial intelligence may be used to assess parts of your application. Please review our privacy policy (see Phenom for details) to learn more about how we collect, use, and disclose your personal information.

Created: Canada, ON, Toronto

Bell, one of Canada's Top 100 Employers.

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More about this job

Responsibilities

The role involves developing, deploying, and maintaining machine learning models and pipelines within a production cloud environment. You will also design infrastructure-as-code and collaborate with product teams to improve model performance and reliability.

Requirements

Candidates must have a bachelor's degree in a relevant field and at least 3 years of experience in machine learning engineering. Proficiency in Python, SQL, cloud platforms, and containerization technologies is required, along with a relevant cloud certification.

Skills

Machine learning
Python
SQL
Cloud computing
CI/CD
DevOps
Docker
Kubernetes
Big data processing
Infrastructure-as-code
Kafka
Apache
Hadoop
Software engineering
Data science
Problem-solving

Benefits

Medical benefits
Dental benefits
Vision benefits
Mental health benefits
35% discount on services
Exclusive partner offers

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Artificial Intelligence
Cloud Infrastructure
AWS
Azure
GCP
Python
SQL
Docker
Kubernetes
CI/CD
DevOps
Infrastructure-as-code
Big Data
Hadoop
Kafka
Apache
Airflow
Argo
Prometheus
Grafana
SDLC
MLLC
Git
Data Science
Software Engineering
Algorithms
Production Environment
Scalability
Reliability

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