The posting
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 experience building, deploying, and supporting enterprise or cloud-native applications.
- Strong full-stack engineering capability using modern frontend, backend, and API technologies such as React, TypeScript, Node.js, Python, .NET, Java, or equivalent.
- Hands-on experience designing and integrating REST APIs, backend services, databases, authentication mechanisms, and enterprise application integrations.
- Practical experience building AI-enabled applications using large language models, RAG, prompt engineering, embeddings, vector search, agents, workflow automation, or AI orchestration frameworks.
- Working knowledge of Azure services including Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Azure App Service, Azure Container Apps, API Management, Key Vault, Azure Monitor, and Log Analytics.
- Experience applying secure software development practices including input validation, secrets management, least-privilege access, dependency scanning, logging, error handling, and secure configuration.
- Understanding of authentication and authorisation standards including OAuth 2.0, OpenID Connect, SAML, JWT, RBAC, and enterprise identity integration using Microsoft Entra ID.
- Hands-on experience with CI/CD pipelines using Azure DevOps, GitHub, GitHub Actions, ShipHATS, or equivalent platforms.
- Familiarity with containerisation, cloud deployment patterns, environment promotion, deployment rollback, and production support practices.
- Ability to assess prototype quality and identify what needs to be rebuilt, hardened, monitored, or redesigned before production release.
- Good understanding of AI application risks including hallucination, data leakage, prompt injection, unsafe tool use, policy bypass, privacy risks, and poor explainability.
- Strong documentation, communication, and stakeholder management skills, with the ability to explain technical decisions and production trade-offs clearly.
- Comfortable working in an agile, product-oriented environment where solutions are delivered iteratively and improved through user feedback and engineering practices.
Nice to Haves
- Experience building chatbots, knowledge assistants, workflow agents, document intelligence solutions, recommendation assistants, or AI-enabled internal tools.
- Experience with AI application frameworks such as LangChain, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or equivalent orchestration tools.
- Experience with vector databases or search technologies such as Azure AI Search, PostgreSQL with pgvector, Cosmos DB, Pinecone, or similar platforms.
- Experience with AI evaluation, prompt testing, red teaming, safety evaluations, grounding quality checks, or responsible AI controls.
- Experience integrating with Microsoft 365, SharePoint, Teams, Microsoft Graph, Power Platform, or enterprise workflow systems.
- Familiarity with public sector cloud environments, government security requirements, data classification, privacy, and compliance obligations.
- Exposure to observability, SRE practices, incident response, service health dashboards, and production support models.
- Experience using AI-assisted development tools such as GitHub Copilot, Microsoft Copilot, Claude, ChatGPT Enterprise, or equivalent tools to improve engineering productivity, testing, and documentation.
Benefits
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