DevOps Engineer – ML & RAG

Role DevOps Engineer

Role Overview: YOE: 5+

Work Mode: 4+1 Hybrid

Number of Openings: 1

Job Description: 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

Bachelors 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 LLM-based applications, including Retrieval-Augmented Generation (RAG)

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)