Job Overview
We are seeking a Full-Stack Engineer with solid .NET development background and practical AI application implementation experience. You will be responsible for the end-to-end development and iteration of AI systems for small and medium-sized localized platforms , including building stable .NET services, integrating local offline LLM capabilities, implementing lightweight full-stack AI features, and ensuring system stability, performance and compliance.
Role Positioning : This is a .NET AI full-stack engineering role focused on application engineering and production delivery, not model training or algorithm research. You will independently implement full-stack AI solutions, covering backend AI service orchestration and frontend AI interactive development.
Basic Qualifications
- Bachelor’s degree or above in Computer Science, Software Engineering or related technical fields.
- 3+ years professional C#/.NET development experience, proficient in .NET Core and WebAPI, with practical experience building and maintaining small-to-medium scale business systems.
- Solid software engineering fundamentals, with hands-on experience in asynchronous programming and performance tuning for local business systems.
- Familiar with network communication principles and socket programming.
- Solid frontend fundamentals with HTML/JavaScript/TypeScript; proficient in at least one mainstream framework (React/Angular/Vue/Blazor).
- Hands-on experience integrating LLM APIs/SDKs to build production AI applications.
- Deep understanding of core AI application technologies: RAG, embedding, vector search and prompt engineering.
- Capable of designing AI service orchestration, exception handling, monitoring and performance tuning.
- Proven experience building full-stack AI scenarios including streaming responses, chat interaction and document Q&A.
- Familiar with AI content security, data privacy compliance and Responsible AI practices.
- Strong independent troubleshooting and cross-team collaboration skills with fluent English for daily technical work.
Preferred Qualifications
- Familiar with lightweight open-source AI frameworks and local LLM integration solutions suitable for small and medium-sized platforms.
- Hands-on experience building lightweight RAG systems, local knowledge bases and internal AI assistants for small and medium-sized platforms.
- Familiar with lightweight vector storage and local retrieval solutions suitable for small and medium-sized platforms, including embedded vector libraries, local file retrieval and lightweight standalone search engines.
- Hands-on experience with WebSocket/SSE streaming interaction and real-time AI full-stack development.
- Familiar with local deployment, standalone service iteration, local environment debugging and system observability.
- Experience in prompt optimization, AI output evaluation and AI application performance tuning is a plus.
- Hands-on experience with C++, Rust, or other system-level programming languages is a plus.
Core Tech Stack
Core Technologies : .NET Core, C#, WebAPI, LLM API/SDK Integration, Prompt Engineering, Embedding, Vector Search, RAG, AI Service Orchestration, Streaming Interaction, Full-Stack AI Development, AI Quality Control & Security Compliance.
Key Responsibilities
- Design and develop stable, lightweight .NET Core services and standardized RESTful APIs to support business and AI scenarios for small and medium-sized localized platforms.
- Build enterprise AI applications based on local LLM services, offline AI SDKs and local AI frameworks; implement end-to-end AI service orchestration, including prompt management, context scheduling, streaming response processing, logging and error handling.
- Build lightweight, deployable local RAG solutions for small and medium scenarios, covering document parsing, embedding generation, local vector retrieval, context assembly and intelligent response generation.
- Implement full-stack AI features such as AI dialogue interfaces, streaming interaction, semantic search and document Q&A; optimize front-backend integration to guarantee system stability and user experience.
- Leverage AI tools to improve full-stack R&D efficiency, including code generation, refactoring, automated testing and rapid prototyping.
- Conduct AI output quality validation, system performance tuning and security optimization; maintain technical documentation and collaborate cross-functionally to drive product and technical iteration.
- Follow standard software engineering and Responsible AI principles to deliver stable, lightweight, and maintainable AI systems for small and medium-sized business platforms.
Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments – powered by our Honeywell Forge software – that help make the world smarter, safer and more sustainable.
