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 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 templates, environment gates, approvals, and automated release promotion workflows.

Hands-on experience integrating AI or LLM capabilities into DevOps workflows for build failure analysis, pipeline summarization, root-cause diagnosis, and recommended remediation.

Experience designing or operating MCP servers, AI-callable tools, function-calling frameworks, or controlled automation interfaces that interact with Azure, ADO, repositories, Kubernetes, or internal APIs.

Practical experience with self-healing or intelligent automation that detects recurring failure patterns, triggers approved remediation actions, and provides diagnostics with clear escalation paths.

Strong working knowledge of Kubernetes-based application deployments, Helm charts, container registries, kubectl troubleshooting, rollout strategies, and secure service account/RBAC practices.

Experience using Azure services such as Azure Container Registry, Azure Key Vault, Service Principals, ARM/Bicep templates, Azure CLI, and Azure Operator Service Manager within automated delivery pipelines.

Familiarity with observability and pipeline intelligence practices, including pipeline health metrics, log analysis, flaky test detection, dashboards, alerts, and continuous improvement feedback loops.

Exposure to GenAI patterns such as prompt engineering, Retrieval-Augmented Generation, tool/function calling, agentic workflows, and responsible use of AI guardrails in automation scenarios.

Ability to lead technical working sessions, document standards and operating procedures, and train client or engineering teams on AI-enabled DevOps pipeline automation.

Nice to Have

Experience with GitHub Copilot extensibility or custom AI agents.

Familiarity with vector databases and Azure AI Search for embedding-based workflows.

Prior work building AI-powered DevOps dashboards or reporting tools.

Experience with Kubernetes-native observability tools such as Prometheus, Grafana, or Datadog.

Additional Job Information