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Applied AI Engineer – C# / .NET
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Applied AI Engineer – C# /.NET
Enterprise SaaS | Permanent | Hybrid
We're partnering with a global enterprise software organisation making a significant investment in Applied AI across an established portfolio of business-critical SaaS products.
This is not a Data Science or AI research role.
You'll be a hands-on Software Engineer taking AI capabilities from idea through to production, embedding LLM-powered functionality into mature C#/.NET products already relied upon by businesses globally.
The Opportunity
You'll work at the intersection of modern.NET engineering and Applied AI, with genuine ownership over how AI features are designed, evaluated and shipped.
You'll be responsible for:
- Building production AI features within established C#/.NET applications
- Integrating commercial LLM APIs into real-world software
- Prompt and context engineering
- Building reliable evaluation around AI outputs
- Designing appropriate fallbacks, monitoring and error handling
- Managing latency, accuracy and token cost
- Working with Product and Engineering to turn genuine customer problems into usable AI functionality
- Prototyping quickly, then engineering solutions properly for production
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Technology
C# |.NET | LLM APIs | Generative AI | RAG | Vector Search | Embeddings | GitHub Copilot | Cursor
What We're Looking For
You'll already be a strong commercial C#/.NET engineer who has moved beyond experimenting with AI and has actually built with LLMs in a professional environment.
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We're particularly interested in engineers who can demonstrate:
- Strong production C#/.NET experience
- Commercial LLM integration
- Prompt/context engineering
- Structured AI outputs
- Evaluation/testing of LLM responses
- Shipping software quickly without compromising production quality
Experience with RAG, agentic workflows, embeddings, vector databases or multiple LLM providers would be highly advantageous.
This is an opportunity to help shape how a major international SaaS organisation brings Applied AI into established products at genuine scale — building technology that reaches real customers rather than producing prototypes that never leave the lab.
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