Software Dev Engineer – Generative AI & Agentic Applications

About this role

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Skills you'll use

What you'll do

• Develop AI applications: Design and build features and services that use LLM and agentic capabilities — prompting, tool/function calling, retrieval-augmented generation (RAG), and orchestration — to solve real internal business problems.
• Write production code: Implement clean, maintainable, well-documented backend and integration code in C# and .NET, following established architecture and coding standards.
• Test thoroughly: Write unit, integration, and end-to-end tests; build and maintain automated test suites; validate output quality and correctness for AI-driven features before release.
• Deploy and operate: Package, deploy, and support applications through CI/CD pipelines; monitor performance, reliability, and cost in production; troubleshoot and resolve issues quickly.
• Integrate with enterprise systems: Connect applications to internal and third-party systems and data sources via REST/JSON APIs, MCP servers, and the iPaaS layer, respecting access controls and data-handling rules.
• Engineer for quality and safety: Implement evaluation, guardrails, logging, and observability so AI features behave predictably and safely at production scale.
• Collaborate cross-functionally: Work with senior engineers, product owners, and business stakeholders to translate requirements into technical designs, and participate actively in code review.
• Document and hand-off: Produce clear technical documentation and runbooks so applications are supportable by the broader team.
• Required Qualifications
• Experience: 6–8 years of professional software development experience, including building and shipping production applications.

Who can apply

• Experience: 6–8 years of professional software development experience, including building and shipping production applications.
• Programming: Strong proficiency in C# and .NET, plus solid REST/JSON API design skills.
• AI application development: Hands-on experience building applications that integrate LLMs — prompting, function/tool calling, RAG, and use of orchestration frameworks such as the Model Context Protocol (MCP).
• Testing: Practical experience with automated testing — unit, integration, and end-to-end — and a disciplined approach to quality assurance.
• Deployment: Experience with CI/CD pipelines (Azure DevOps), containerization (Docker), and deploying and operating applications in the cloud.
• Databases: Working experience with SQL and NoSQL databases (e.g., MongoDB); comfortable design schemas and writing efficient queries.
• Cloud fundamentals: Solid experience with Microsoft Azure, version control (Git), and observability/monitoring practices.
• Problem solving: Demonstrated ability to break down ambiguous requirements into a working, tested, and deployable solution.

About SonicWall

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