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Added 13 mins ago
Data Engineer - Analytics
Capgemini
Montreal, QC
Engineer observability solutions that proactively monitor configuration changes, control performance, operational trends, risk signals, and testing outcomes.
Added 13 mins ago
HR
AI Forward Deployed Engineer - CRM & Marketing
Hard Rock Digital
Remote, Canada
- Full-time
- Remote
We're hiring an AI Forward Deployed Engineer to build agentic and generative AI for CRM and Marketing. You'll ship production features, not prototypes, that help us acquire, engage, and…
Added 26 mins ago
M
Spécialiste infonuagique et DevOps / Cloud & DevOps Specialist
Montrium
Montreal, QC
- Full-time
Added 26 mins ago
AT
Senior Data Engineer
Arkhya Tech Inc.
Ottawa, ON
- Contract
Previous experience leading an enterprise-wide Cloud Data Platform migration with strong architectural and design skills.
Added 26 mins ago
VC
Python Developer
VBeyond Corporation
Toronto, ON
- Full-time
Backend Engineer – Python (API & Platform Engineering)
Added 28 mins ago
T
Software Engineer I
TD
Toronto, ON
- Full-time
The Risk Technology team within the Finance and Risk Platform group supports non-external-facing credit risk applications used for the Bank’s regulatory capital calculations and reporting…
Added 42 mins ago
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Senior Data Engineer
Scene+
Toronto, ON
- Full-time
Reporting to the Senior Manager, Data Engineering and Data Governance, we are looking for a Senior Data Engineer who will play a pivotal role in shaping the next generation of Scene+’s data…
Added 1 hour ago
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Cloud Architect
Agilus Work Solutions
Toronto, ON
- $60 to $80 CAD / hour
- Contract
We are looking for a Senior Business Intelligence Engineer to support the development of a Consolidated Ad Revenue Reporting platform. This role sits at the intersection of Data…
Added 1 hour ago
Data Scientist (Askuity division)
The Home Depot
Toronto, ON
Élaborer, évaluer et déployer des modèles d’apprentissage automatique dans Vertex AI (Google Cloud Platform) avec des pratiques d’opérations d’apprentissage automatique de qualité…
Added 1 hour ago
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QA Automation Engineer (Askuity division)
The Home Depot
Toronto, ON
- Full-time
We are looking for a dynamic QA Automation Engineer, eager to deliver high quality SaaS based products. As a member of the Product Development team at Askuity, you will help design and…
Added 1 hour ago
IF
Data Engineer, Investments
iA Financial Group
Montreal, QC · Quebec, QC
- Full-time
As a Data Engineer, you will play a key role in the design, industrialization, and optimization of modern data pipelines on the Google Cloud platform. You will actively contribute to…
Added 1 hour ago
M
Cloud & AI Solution Engineer
Microsoft
Toronto, ON
- Full-time
Are you curious, enthusiastic about infrastructure, and ready to solve complex challenges in the AI era? Join us as a Cloud & AI Solution Engineer - Azure Platform for enterprise and…
Added 1 hour ago
M
Innovative Data Platform Solution Engineer
Microsoft
Toronto, ON
- Full-time
…in the era of AI? If so, we invite you to join our team as a Cloud & AI Solution Engineer in Innovative Data Platform for commercial customers at Microsoft. Here, you'll be at the forefront…
Added 3 hours ago
Senior Security Engineer, Threat Response
Asana
Vancouver, BC
We are looking for a savvy Security Engineer to join our Blue Team. You will be a foundational member of the security presence in our Warsaw innovation hub, partnering directly with IT,…
Added 3 hours ago
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Senior React Native Developer
AI Interviews
Toronto, ON · Greater Toronto Area, Canada
- Contract
We are seeking a Senior/Lead React Native Engineer with 8+ years of mobile engineering and 5+ years of production React Native experience.Strong expertise in React Native 0.76+, New…
Added 4 hours ago
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[C] Salesforce Engineer / Developer
MLSE (Maple Leaf Sports & Entertainment Partnership)
Toronto, ON · Scotiabank Arena
- Part-time
The successful candidate will bring deep Salesforce platform experience and extensive hands-on experience implementing, supporting, and enhancing Salesforce Loyalty Cloud / Loyalty…
Added 5 hours ago
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Développeur(-euse) de logiciels sénior(e) (contenu) / Senior Software Development Engineer
Expedia Group
Montreal, QC · Canada - Quebec - Montreal
- Full-time
Added 5 hours ago
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Senior Software Engineer, Applied AI & Customer Solutions
Udemy
Toronto, ON
- Full-time
As a Senior Software Engineer, you will join a fast-paced innovation team that builds and deploys AI-powered solutions directly with Coursera's enterprise and campus customers. You will sit…
Added 5 hours ago
Software Engineer, Web3 Backend
Robinhood
Toronto, ON
As a Software Engineer, you’ll build and own backend services, infrastructure, and data systems that support Robinhood’s next-generation crypto, web3, and wallet offerings. You’ll develop…
Added 5 hours ago
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Backend Platform Engineer - Hapiko
Spin Master
Toronto, ON
- Full-time
You'll own everything behind that moment: the real-time connection layer, the API, the job pipeline and the cloud infrastructure every device talks to. We're a small team building a…
Added 6 hours ago
IC
Cloud Infrastructure & DevSecOps Engineer
InfoTrack Canada
Toronto, ON
- Full-time
As the Cloud Infrastructure and DevSecOps Engineer, you will own the end-to-end cloud infrastructure and deployment pipeline for InfoTrack Canada. You will unblock critical environment…
Added 7 hours ago
C
AKS/ ARO DevOps Engineer
Capgemini
Mississauga, ON
- $75 to $95 CAD / hour
Develop reusable Terraform/Bicep modules and pipeline templates for platform and workload deployment.
Added 7 hours ago
F
Senior Software Engineer
Fleetworthy
AB · Edmonton, AB
- Full-time
Familiarity with cloud platform services such as Azure or AWS
Added 8 hours ago
M
Senior Full Stack Java Engineer - CX & Contact Center Solutions
Miratech
Canada
- Full-time
We are looking for a Senior Full Stack Engineer to provide hands-on technical leadership across CX and contact center solutions. You will shape scalable solutions using Java, Spring Boot,…
Added 8 hours ago
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Senior Salesforce Engineer
Canadian Cancer Society
Prince George, BC
- Full-time
We are seeking a hands-on Salesforce Platform and AI Engineer to design, build, and scale secure, data-driven solutions that advance CCS’s mission. This role combines Salesforce…

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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