Specialist System Engineering

About the Role

This is an offshore role requiring daily collaboration with U.S. stakeholders, including overlapping work hours.

Key Responsibilities

• Design, build, and maintain Azure DevOps CI/CD pipelines for 5G NF application deployment.
• Develop AI-assisted pipeline automation capabilities using LLMs for analysis of build failures, deployment issues, pipeline health, and operational telemetry.
• Build and maintain MCP servers and AI-callable tool integrations to expose Azure, Azure DevOps, Kubernetes, repository, and deployment capabilities.
• Create self-healing pipeline workflows to detect failure patterns, trigger remediation, and provide diagnostics.
• 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.
• Integrate pipeline workflows with source control, artifact repositories, approvals, environment gates, testing frameworks, security checks, and release governance processes.
• Support Kubernetes-based deployments, troubleshooting failures, configuration issues, container readiness, service health, and environment-specific behavior.
• Implement observability for CI/CD systems, including metrics, logs, dashboards, alerts, and failure trend analysis.
• 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.
• Lead technical discussions, working sessions, demos, and hands-on training to improve DevOps maturity and enable adoption of AI-enabled pipeline automation.
• Document architecture, operating procedures, automation patterns, troubleshooting guides, and standards.
• Continuously evaluate emerging DevOps, GenAI, LLM, and MCP capabilities and recommend enhancements.

Requirements & Tech Stack

• Linux & Scripting Fundamentals
• Expert-level Linux experience.
• Strong scripting skills in Bash, Python, and/or PowerShell.
• 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 scratch using YAML.
• Proficiency with az cli commands within ADO Pipelines.
• Deep understanding of ADO Repos including branching, tagging, and environment management strategies.
• Working knowledge of ADO Agents.
• Strong use of JSON and YAML as data formats.
• Azure Platform & Infrastructure
• Experience with Azure Container Registry (ACR).
• Understanding of Azure Resource Manager, Endpoints, and Service Principals.
• Ability to build Azure Resources using Bicep and ARM Templates.
• 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.
• Ability to diagnose and resolve common deployment failures.
• Experience integrating Kubernetes deployment steps into ADO Pipelines.
• Familiarity with Helm charts.
• Understanding of Kubernetes RBAC, service accounts.
• AI & LLM Integration
• Hands-on experience integrating Azure OpenAI or equivalent LLM APIs.
• Ability to design prompts for pipeline analysis, failure summarization, and root-cause diagnosis.
• Familiarity with agent-based AI patterns like tool/function calling and Retrieval-Augmented Generation (RAG).
• Experience designing and building custom MCP servers.
• 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.
• Communication & Collaboration
• Demonstrated ability to articulate complex technical concepts and solutions.
• Collaborative approach working across platform, security, and application engineering teams.

Qualifications

• 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.

• Experience designing or operating MCP servers, AI-callable tools, function-calling frameworks, or controlled automation interfaces.

• Practical experience with self-healing or intelligent automation.

• 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.

• Familiarity with observability and pipeline intelligence practices.

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

• Ability to lead technical working sessions, document standards and operating procedures, and train client or engineering teams.

• Nice to Have

• Experience with GitHub Copilot extensibility or custom AI agents.

• Familiarity with vector databases and Azure AI Search.

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

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