Software Engineer II

Job description

Description and Requirements 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.