Build and ship across the full stack (frontend, backend, services, and data pipelines), taking end-to-end ownership of features rather than being limited to a single layer or specialty. Use AI tooling and coding assistants (such as GitHub Copilot and agentic coding workflows) as part of your everyday engineering to accelerate delivery, improve code quality, and expand what you can own single-handedly. Apply large language models (LLMs) and generative AI to product and engineering problems: prototyping, evaluating, and integrating AI-powered capabilities, prompts, and automation into our app, web, and service surfaces. Design and maintain automation and test harnesses that make our build, validation, and release processes faster and more reliable, and continuously look for opportunities to automate manual work. Own a workflow area end to end (such as platform, accessibility, security, or service health), defining best practices, tooling, and quality bars, and driving measurable improvement in that area. Modernize how we build and deploy on Azure using Infrastructure-as-Code (IaC) and CI/CD pipelines, and integrate quality, security (SDL), privacy, and accessibility gates directly into those pipelines. Proactively monitor reliability and performance using Azure Monitor and similar tooling, define SLIs/SLOs, build service-health dashboards, and participate in the on-call rotation for the services millions of customers depend on. Drive work to closure with urgency, stay focused on the highest-impact priorities, share what you learn about AI and automation with the team, and communicate status, risks, and decisions proactively across partner teams. Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Hands-on experience using AI-powered development tools (such as GitHub Copilot or comparable coding assistants) and a demonstrated ability to adopt new AI tooling to improve how you build and ship software. Experience building software across more than one layer of the stack (frontend, backend, services, or data) and a willingness to work in any stack as the problem requires. Experience building and operating cloud-based services on Azure (or a comparable platform), including CI/CD, automation, and production monitoring, while owning a key engineering area such as platform, security, accessibility, privacy, or service health. Demonstrated individual ownership: driving work to completion with urgency, adapting quickly to new tools and domains, and communicating clearly and proactively with partners. Experience building automation and test harnesses, or developer-productivity and agentic workflows that reduce manual effort across the engineering lifecycle. Experience with observability and live-site operations: Azure Monitor or Application Insights, service-health dashboards, SLIs/SLOs, and on-call rotations. Experience applying large language models (LLMs) in software: prompt engineering, model integration, retrieval-augmented generation (RAG), or evaluating and productionizing AI features.
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