DevOps Engineer

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.