About the Role
The DevOps Engineer will play a critical role in operationalizing artificial intelligence across Bell Techlogix client environments. This role focuses on building and supporting cloud infrastructure, CI/CD pipelines, and automation frameworks that power AI and machine learning workloads. The ideal candidate has experience supporting AI platforms such as Azure AI, Azure Machine Learning, Azure OpenAI, and ServiceNow or conversational AI platforms, and understands the operational requirements of production AI systems, including reliability, scalability, and security.
Key Responsibilities
- Design, build, and operate cloud infrastructure and platform services that support AI and machine learning workloads in production, SLA-driven managed services environments.
- Implement CI/CD and MLOps pipelines to enable automated training, testing, deployment, and rollback of AI and ML models.
- Develop and maintain Infrastructure as Code to provision AI-ready environments consistently across dev/test/prod.
- Support AI platform operations including monitoring model health, pipeline execution, compute utilization, and data dependencies.
- Partner with Machine Learning Engineers and Data Engineers to standardize deployment patterns for AI services and LLM-based solutions.
- Enable secure and scalable AI integrations using APIs, messaging, and event-driven architectures.
- Implement observability solutions for AI platforms, including logging, metrics, alerting, and drift detection integrations.
- Troubleshoot AI platform incidents, perform root cause analysis, and implement remediation to improve reliability and automation coverage.
- Apply security best practices for AI environments including secrets management, identity and access controls, network isolation, and policy enforcement.
- Support AI-driven automation use cases across platforms such as Microsoft Copilot, ServiceNow, and conversational AI tools.
- Collaborate with service desk, security, and architecture teams to continuously improve AI service delivery and operational maturity.
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
- 5+ years of experience in DevOps, cloud engineering, or platform operations, with exposure to AI or data workloads.
- Hands-on experience with Microsoft Azure, including compute, networking, storage, and monitoring services.
- Experience building CI/CD pipelines using Azure DevOps, GitHub Actions, or similar tools.
- Working knowledge of Infrastructure as Code (Terraform and/or Bicep/ARM).
- Scripting experience using PowerShell and/or Python.
- Experience supporting production platforms with incident management, change control, and root cause analysis.
- Understanding of cloud security fundamentals and enterprise governance requirements.
Preferred Qualifications
- Experience with Azure Machine Learning, Azure AI Services, Azure OpenAI, or MLOps frameworks.
- Exposure to containerization and orchestration technologies (Docker, Kubernetes, AKS).
- Experience supporting data pipelines or feature stores used by machine learning systems.
- Familiarity with ServiceNow, AI-driven ITSM workflows, or automation platforms.
- Experience with observability tools.
- Knowledge of Responsible AI, data governance, and compliance considerations for AI systems.
- Relevant certifications (Microsoft Azure Administrator, Azure DevOps Engineer, Azure AI Engineer).