AI Engineer

Job description

Role Description The AI Engineer (Full Stack Engineering) designs, develops, tests, deploys, and maintains modern cloud-native applications using an AI-first engineering model. This role requires strong full-stack capability, upfront architecture and specification skills, responsible use of AI coding assistants, and ownership of quality, security, reliability, and production readiness. Key Responsibilities • Apply AI-first engineering practices across discovery, requirements, design, development, testing, deployment, operations, and continuous improvement. • Translate business outcomes into clear technical specifications, architecture decisions, NFRs, design notes, and acceptance criteria. • Build secure, scalable, resilient, observable, and maintainable full-stack solutions. • Use AI responsibly to improve engineering productivity and software delivery quality. • Partner with product, architecture, security, operations, and business teams to deliver aligned outcomes. • Support incident resolution, automation, simplification, and continuous improvement activities. Candidate Qualifications • Bachelor’s degree in Computer Science, Engineering, Information Technology, or equivalent experience. • At least 3 years of experience developing enterprise applications using modern full-stack, API, data, and cloud technologies. • Hands-on experience with AI-assisted software delivery, GitHub Enterprise, and GitHub Copilot. • Exposure to Azure AI Foundry, Copilot SDK, Semantic Kernel, AI agents, RAG, and LLM application patterns is preferred. • Strong knowledge of Agile, DevOps, CI/CD, automated testing, API-first development, domain-driven design, and shift-left quality and security practices. • Ability to clearly articulate requirements, design intent, constraints, trade-offs, and outcomes for people and AI-assisted platforms. Tech Stack • AI & Developer Productivity: GitHub Enterprise, GitHub Copilot, Azure AI Foundry, Microsoft Copilot ecosystem, Copilot SDK, Semantic Kernel, AI agents, RAG, LLM patterns. • Cloud & Architecture: Microsoft Azure, cloud-native architecture, microservices, APIs, containers/Kubernetes, event-driven architecture, observability. • Delivery & Engineering: Azure DevOps, Git, CI/CD, automated testing, SonarQube, secure coding, infrastructure as code, Agile, DDD, API-first, ADRs, NFRs. • Development & Operations: Java, Spring Boot, ReactJS, HTML, JavaScript, mobile frameworks, SQL/NoSQL, authentication/authorization, API management, Veracode, Azure Application Insights, Elastic, logging, and monitoring.