Sr Specialist System Engineering - DevOps Engineer — AI & Pipeline Automation

Job description

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 pipelines • Understanding of Kubernetes RBAC, service accounts, and their role in secure pipeline-based deployments AI & LLM Integration • Hands-on experience integrating Azure OpenAI or equivalent LLM APIs into automation workflows • Ability to design prompts for pipeline analysis, failure summarization, and root-cause diagnosis • Familiarity with agent-based AI patterns including tool/function calling and Retrieval-Augmented Generation (RAG) • Experience designing and building custom MCP servers to expose internal APIs and data as AI-callable tools Pipeline Intelligence & Analysis • Ability to leverage ADO REST APIs to surface pipeline health metrics, flaky tests, and failure patterns • Experience building AI-assisted workflows for root-cause analysis against pipeline logs • Capability to design self-healing pipeline logic - detect, diagnose, remediate, and re-trigger Communication & Collaboration • Demonstrated ability to articulate complex technical concepts, solutions, and standards to stakeholders across varying skill levels - through both presentations and discussions • Collaborative approach to working across platform, security, and application engineering teams Preferred Qualifications • Minimum bachelor’s degree in Computer Science, Electronics and Communication, Engineering, Information Technology, or a related technical discipline. • Demonstrated experience building enterprise-grade Azure DevOps pipeline automation using YAML, reusable templat