AI Application Engineer - (Public Sector)

At Xtremax, we are looking for an AI Application Engineer to help turn promising AI prototypes into secure, production-ready applications. You will work on AI-enabled solutions such as chatbots, RAG applications, workflow assistants, agents, automation tools, and AI-powered business applications. You will work across the full application lifecycle, from understanding business outcomes and assessing early prototypes to engineering, testing, deployment, observability, and production support. The role combines strong software engineering fundamentals with practical AI application development, giving you the opportunity to work with technologies including Azure OpenAI, Azure AI Foundry, Azure AI Search, Microsoft Entra ID, Microsoft Graph, React, TypeScript, Node.js, Python, .NET, Java, and modern CI/CD platforms . This is a hands-on engineering role for someone who enjoys working at the intersection of experimentation and production. You will help establish reusable engineering patterns for AI applications while ensuring that security, privacy, responsible AI, observability, cost management, and long-term supportability are built into solutions from the start. Responsibility Refactor prototypes and AI-assisted applications into secure, maintainable, production-grade solutions with clean architecture, robust APIs, authentication, and automated deployment pipelines. Design, build, and harden AI-enabled applications including chatbots, RAG solutions, workflow assistants, agents, automation tools, and AI-assisted business applications. Work with product owners and business stakeholders to translate use cases into clear user journeys, measurable outcomes, adoption metrics, and production-readiness requirements. Develop full-stack application capabilities across frontend, backend, APIs, databases, data integrations, authentication, authorization, logging, and monitoring. Integrate applications with approved AI and enterprise services such as Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Microsoft Graph, and enterprise APIs. Apply secure and responsible AI patterns including prompt management, retrieval grounding, input/output controls, human-in-the-loop workflows, auditability, and content safety controls. Develop reusable AI application patterns, starter templates, and engineering playbooks to accelerate delivery across different use cases. Build automated testing for AI applications, including functional and regression testing, prompt evaluation, response quality checks, and guardrail validation. Implement application and AI observability covering logs, model usage, latency, token consumption, errors, user feedback, cost, and key business metrics. Collaborate with platform engineering teams on cloud deployment, CI/CD, containerisation, API management, secrets management, monitoring, production support, and operational handover. Requirements 7+ years of hands-on software engineering experience, including…