A .NET AI Engineer bridges the gap between traditional enterprise backend software engineering and modern Generative AI ecosystem integration. Unlike standalone AI engineers who work primarily in Python, a .NET AI Engineer builds, scales, and deploys intelligent applications directly within the Microsoft .NET Core and Azure ecosystems.
Below is a production-ready, highly functional Job Description template tailored for this specialized role.
Job Description: .NET AI Engineer
Job Summary
We are seeking a highly skilled .NET AI Engineer to drive the development of our next-generation intelligent applications and enterprise automation workflows. In this role, you will be responsible for architecting and building agentic AI systems, conversational workflows, and retrieval-augmented systems inside our ecosystem. You will combine your deep expertise in C# and .NET Core with cloud-native Azure AI and Large Language Model (LLM) APIs to translate bleeding-edge AI models into stable, production-grade enterprise software.
Key Responsibilities
- AI Application Architecture: Design, develop, and scale AI-powered backend systems and REST APIs utilizing .NET 8+, ASP.NET Core, and microservices.
- LLM & Agentic Orchestration: Integrate LLMs (e.g., Azure OpenAI, GPT-4, Claude) using tools like Semantic Kernel or LangChain/.NET to build autonomous multi-step reasoning agents.
- Context & RAG Pipelines: Architect and manage Retrieval-Augmented Generation (RAG) frameworks using vector databases (e.g., Pinecone, Qdrant, Azure AI Search).
- Data Integration: Build and maintain high-throughput enterprise data flows, connecting structured relational data (SQL Server) with unstructured embeddings.
- Prompt Engineering & Tooling: Implement precise function calling, Model Context Protocol (MCP) toolkits, and dynamic system prompts to securely expose enterprise systems to AI models.
- AI Observability & Ops: Establish production metrics, guardrails, cost logging, and telemetry monitoring for LLM utilization, token consumption, and response accuracy.
Required Technical Skills
- Core Programming: C# and .NET Core / .NET 6+ (Deep proficiency with asynchronous programming patterns, LINQ, and multi-threading).
- AI Engineering Frameworks: Practical experience with Microsoft Semantic Kernel, Azure AI SDKs, or ML.NET.
- Cloud Infrastructure: Extensive experience with Azure Cloud Services (Azure OpenAI, Cognitive Services, Azure Functions, API Management).
- Data & Search: Command over SQL, entity indexing, embedding strategies, and vector query mechanics.
- Modern API Standards: Robust knowledge of web hooks, RESTful API architecture, gRPC, and JSON orchestration.
- Secondary Scripting: Comfortable reading or writing Python for machine learning pipeline interfacing and data preprocessing.
Qualifications & Experience
- Education: Bachelor’s degree in Computer Science, Software Engineering, Data Science, or an equivalent technical field.
- Experience: 3+ years of professional software engineering experience using the .NET stack, including at least 1 year actively shipping LLM- or AI-enabled tools into consumer or enterprise production.
- Methodology: Deep understanding of secure CI/CD pipelines, containerization (Docker/Kubernetes), and Agile workflows.