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Added 7 mins ago
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Director, Technical Support - Cybersecurity
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Bell

AI Engineer

Toronto, ON, Canada

Hybrid

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Job details

Workplace: Hybrid
Apply by: Oct 22, 2026
Experience: 2 to 4 years of professional software engineering or data science experience, including at least 1 to 2 years of hands-on experience building with LLMs and agentic architectures.
Posting language: English
Posted: Oct 5, 2026

Original job description

Req Id: 433042

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.

Summary

The Team

The Applied AI & Data Science team builds and deploys production-grade AI solutions across Bell. We bridge the gap between cutting-edge AI research and operational enterprise systems, delivering autonomous agentic workflows, conversational intelligence, and machine learning systems that transform our internal operations and customer experiences.

Position Description

We are looking for a hands-on AI Engineer (CP2) to design, build, and deploy the next generation of AI-driven workflows and autonomous systems at Bell.

In this role, you will work closely with data scientists, senior engineers, and business squads to turn prototypes into reliable, production-ready AI applications. You will focus on building agentic workflows, integrating tools and enterprise services via modern protocols (such as Model Context Protocol), and connecting foundation models to enterprise data sources. The ideal candidate combines a strong software engineering foundation with a practical, builder mindset in the rapidly evolving LLM and agent ecosystem.

Key Responsibilities

Build & Ship Agentic Applications: Develop, test, and deploy stateful, multi-step agentic workflows and tool-calling systems that automate complex operational and analytical processes.
Enterprise System Integration: Implement robust APIs, connectors, and protocols (such as Model Context Protocol / MCP) to securely connect AI agents to internal services, databases, and enterprise platforms.
Data & Knowledge Grounding: Build and maintain scalable data retrieval pipelines over structured and unstructured data, utilizing vector search and retrieval-augmented (RAG) architectures to ensure model accuracy and contextual awareness.
Squad Collaboration: Partner with cross-functional squads (data scientists, product managers, and enterprise architects) to take solutions from rapid proof-of-concept to production deployments.
Reliability & Quality: Implement evaluation metrics, automated unit/integration tests, and observability to monitor model performance, latency, cost, and reliability in production.

Critical Qualifications

A degree in Computer Science, Engineering, or a related field.

2 to 4 years of professional software engineering or data science experience, including at least 1 to 2 years of hands-on experience building with LLMs and agentic architectures.
Strong software engineering foundation: Proven proficiency in Python, clean code architecture, robust API design, and integration with production enterprise systems.
Hands-on experience with modern AI patterns: Practical familiarity with tool/function calling, agent orchestration patterns, and retrieval-augmented generation (RAG) over enterprise data.
Cloud & Data Systems: Experience deploying and operating applications on a major cloud platform (e.g. Google Cloud Platform) and working with structured/unstructured databases.
Continuous Learning: An active habit of staying current with fast-moving foundation model capabilities, agent protocols, and developer tooling.

Preferred Qualifications

Experience deploying AI applications within enterprise environments (e.g. GCP, microservices architecture, enterprise SaaS integrations).
Familiarity with emerging open standards in AI tooling (e.g. Model Context Protocol / MCP, agent evaluation frameworks).
Practical exposure to LLMOps / MLOps practices, including prompt evaluation, trace logging, CI/CD, and containerized deployments.
Experience collaborating with or working alongside data science teams to productionize analytical pipelines.
A public GitHub portfolio or open-source contributions demonstrating hands-on experimentation with agents or LLMs.

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 || Canada : Ontario : Mississauga
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 AI Engineer will design, build, and deploy production-grade agentic workflows and autonomous systems. They will collaborate with cross-functional teams to integrate foundation models with enterprise data sources and ensure system reliability.

Requirements

Candidates must have a degree in Computer Science or Engineering and 2 to 4 years of professional experience, including 1 to 2 years working with LLMs. Proficiency in Python, API design, and cloud-based data systems is required.

Skills

Python
LLM
Agentic workflows
RAG
Machine learning
API design
GCP
Vector search
Model Context Protocol
Software engineering
Data science
System integration
MLOps
Prompt evaluation
Containerization
CI/CD

Benefits

Medical benefits
Dental benefits
Vision benefits
Mental health benefits
Service discount
Exclusive partner offers

Visa sponsorship

Not detected in the job text

Keywords

AI Engineer
Python
LLM
Agentic workflows
RAG
Machine learning
GCP
API
Model Context Protocol
Vector search
Data science
MLOps
CI/CD
Containerization
Enterprise systems
Software engineering
Automation
Observability
Prompt evaluation
Microservices
Cloud computing
Data retrieval
Foundation models
Production deployment
Scalable architecture

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