About this role
Req Id: 428729Connection 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.Become an integral part of our Customer Experience team, where you will be at the forefront of shaping our customer journeys. You will build on Bell's Strategic Imperative to Champion Customer Experience and reinforce our commitment to put the customer first in everything we do. Design innovative, customer-centric processes, policies, products and services, while upholding our Customer-First Commitments to make it easier for customers to do business with Bell. As part of our team, you will be at the heart of our customer experiences.SummaryAs an Intermediate AI DevOps Engineer you are a skilled practitioner responsible for making the AI enablement vision a reality. This role involves supporting and executing within established frameworks to implement AI best practices, drive adoption, and ensure seamless AI integration across various business units (BUs).Key ResponsibilitiesAI Standardization & Adoption: Implement AI best practices and actively support their adoption across engineering teams.Cross-BU Collaboration: Work closely with different BUs' teams to ensure successful integration of AI capabilities and MLOps practices.MLOps Implementation & Configuration: Set up and configure MLOps tools, CI/CD pipelines, and infrastructure components according to established architectural designs.Developer Support: Directly assist development teams in utilizing enablement capabilities, troubleshooting pipeline issues, and providing guidance on best practices for AI/ML development.Documentation Contribution: Create and update user-facing documentation, tutorials, and examples for MLOps tools, processes, and AI enablement resources.Scripting & Automation: Develop scripts to automate routine tasks for both the enablement team and development teams, enhancing efficiency.Feedback Gathering: Systematically collect feedback from development teams regarding the usability and effectiveness of enablement resources and tools.CompetenciesMLOps Execution: Ability to implement and configure MLOps tools and CI/CD pipelines as designed.Technical Support: Skill in assisting developers, troubleshooting technical issues, and guiding them on best practices.Automation Development: Proficiency in scripting (e.g., Python, Bash) to automate routine tasks.Documentation: Capability to create and update clear, user-friendly technical documentation.Collaboration: Ability to work effectively with cross-functional teams and gather constructive feedback.Problem-Solving: Aptitude for identifying and resolving technical issues within established frameworks.·AI/ML Workflow Understanding: Foundational knowledge of AI/ML concepts and the importance of DevOps in their lifecycleCritical QualificationsBachelor's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.3+ years of experience in DevOps, SRE, or Software Engineering roles, with exposure to AI/ML development workflows.Hands-on experience configuring and managing CI/CD pipelines.Experience with scripting languages such as Python or Bash.Exposure to MLOps tools and concepts (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).Experience creating technical documentation.Understanding of Git for version control.Familiarity with containerization technologies like Docker.Basic knowledge of AI/ML co