Software Dev Engineer Generative AI And Agentic Applications

Position Summary 

We are hiring a Software Dev Engineer to design, build, test, and deploy AI-powered applications. You will work across the full application lifecycle — from architecture and implementation through automated testing, CI/CD deployment, and production monitoring — building features that put large language models and agentic tooling to work inside SonicWalls internal systems. 

This is a hands-on engineering role for someone with 6–8 years of professional software development experience who is comfortable owning services end to end: writing production-quality code, integrating LLM and agentic APIs, standing up reliable data and retrieval pipelines, and shipping through a disciplined test-and-release process. You will collaborate closely with senior engineers, product stakeholders, and platform teams to turn requirements into dependable, well-tested applications. 

Key Responsibilities 

• 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.
• 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.
• Communication: Strong written and verbal English; able to explain technical trade-offs clearly to both engineers and non-technical stakeholders. 

Preferred Qualifications 

• Agentic tooling: Experience with the Model Context Protocol (MCP), LangChain, LangGraph, or comparable agentic orchestration frameworks.
• AI platform: Familiarity with Azure AI Foundry (good to have).
• LLM platforms: Practical experience with Anthropic Claude, Claude Code, or the Claude Agent SDK, or equivalent LLM provider platforms.
• Vector search: Experience with vector databases (e.g., Pinecone, Weaviate) and retrieval pipeline design.
• Containers & orchestration: Familiarity with Kubernetes (e.g., Azure Kubernetes Service) or similar container-orchestration platforms.
• Enterprise applications: Familiarity with Salesforce, Snowflake, Oracle EBS, or similar enterprise application landscapes.
• Industry background: Experience in security, SaaS, or B2B technology sectors. 

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