Specialist System Engineering - DevOps Engineer — AI & Pipeline Automation

Job Responsibilities

• Design, build, and maintain Azure DevOps CI/CD pipelines that support 5G NF application deployment from lab environments through production with repeatable, secure, and automated promotion workflows.

• Develop AI-assisted pipeline automation capabilities that use LLMs to analyze build failures, deployment issues, pipeline health, and operational telemetry.

• Build and maintain MCP servers and AI-callable tool integrations that expose Azure, Azure DevOps, Kubernetes, repository, and deployment capabilities through controlled automation interfaces.

• Create self-healing pipeline workflows that can detect common failure patterns, trigger approved remediation steps, and provide clear diagnostics, confidence levels, and escalation paths.

• Partner with client project teams to understand delivery requirements, architect CI/CD solutions, and implement automation patterns aligned with enterprise DevOps standards.

• Develop reusable YAML templates, pipeline components, scripts, and automation libraries that standardize CI/CD delivery across applications, environments, and teams.

• Integrate pipeline workflows with source control, artifact repositories, approvals, environment gates, testing frameworks, security checks, and release governance processes.

• Support Kubernetes-based deployments by troubleshooting deployment failures, configuration issues, container readiness, service health, and environment-specific pipeline behavior.

• Implement observability for CI/CD systems, including pipeline metrics, logs, dashboards, alerts, failure trend analysis, and continuous improvement feedback loops.

• Apply secure DevOps practices for secrets handling, access control, policy enforcement, code review, vulnerability checks, and audit-ready release execution.

• Create AI-assisted root-cause analysis tools and knowledge workflows that help engineering teams quickly identify probable causes and recommended next actions.

• Lead technical discussions, working sessions, demos, and hands-on training to improve DevOps maturity and enable client teams to adopt AI-enabled pipeline automation.

• Document architecture, operating procedures, automation patterns, troubleshooting guides, and standards to ensure consistent adoption and long-term maintainability.

• Continuously evaluate emerging DevOps, GenAI, LLM, and MCP capabilities and recommend practical enhancements that improve delivery speed, quality, reliability, and operational efficiency.

Job Qualifications / Required Qualifications

Linux & Scripting Fundamentals

• Expert-level Linux experience with strong scripting skills in Bash, Python, and/or PowerShell

• Proven ability to automate manual processes using scripting languages and Infrastructure as Code (e.g., Ansible)

• Hands-on experience containerizing, deploying, debugging, and maintaining applications

Azure DevOps & Pipeline Engineering

• Expert ability to build ADO Pipelines from the ground up using YAML

• Proficiency with az cli commands within ADO Pipelines to interact with Azure Resources

• Deep understanding of ADO Repos including branching, tagging, and environment management strategies

• Working knowledge of ADO Agents – their purpose, capabilities, and limitations

• Strong use of JSON and YAML as data formats across scripts, Ansible playbooks, and pipelines

Azure Platform & Infrastructure

• Experience with Azure Container Registry (ACR) to import, tag, and extract images and charts within pipelines

• Understanding of Azure Resource Manager, Endpoints, and Service Principals

• Ability to build Azure Resources using Bicep and ARM Templates with emphasis on parameterization

• Familiarity with Azure Key Vault (AKV) and Hashi Corp Enterprise Vault (HCEV) for secrets management

• Experience with Azure Operator Service Manager (AOSM)

Kubernetes & Container Orchestration

• Hands-on experience deploying, managing, and debugging workloads on Kubernetes (AKS preferred)

• Proficiency with kubectl for inspecting pods, logs, events, and resource states during pipeline-triggered deployments

• Ability to diagnose and resolve common deployment failures including CrashLoopBackOff, image pull errors, resource quota issues, and failed health probes

• Experience integrating Kubernetes deployment steps into ADO Pipelines including rollout strategies, namespace management, and environment promotion

• Familiarity with Helm charts for packaging and deploying applications through pi…