AI Engineer ( .Net )

This role goes beyond model building - you'll be hands-on with Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), and cloud-native architectures, while embedding best practice in AI governance, security, and performance. It's an opportunity to drive real-world adoption of AI, from proof-of-concept through to production-scale deployment.

What You'll Do

Design and build AI-driven applications using LLMs, RAG architectures, and modern vector databases

Fine-tune, deploy, and integrate LLMs (OpenAI, Hugging Face, Anthropic, etc.) into enterprise platforms

Implement safeguards to protect against data leaks, prompt injections, and adversarial attacks

Develop APIs and pipelines for smooth AI deployment and integration across enterprise systems

Work within .NET and cloud-native environments (Azure, AWS, GCP) to integrate AI into existing applications

Ensure solutions meet compliance, governance, and ethical standards (e.g. GDPR, ISO 27001, EU AI Act)

Optimise models for performance, scalability, and cost-effectiveness

Collaborate with product, compliance, and security teams to align AI delivery with business outcomes

Stay ahead of emerging AI ethics, regulation, and observability trends

Conduct risk assessments and build robust monitoring into deployed solutions

Hands-on experience building and deploying AI/ML models in production

Strong background with RAG pipelines and vector search tools (FAISS, Pinecone, Weaviate)

Expertise in LLMs and NLP - tuning, integrating, and securing models

Proficiency in Python and/or C#, with strong programming and integration skills

Knowledge of .NET environments and Microsoft-based infrastructure

Cloud engineering experience (Azure, AWS, or GCP) with MLOps best practices

Strong understanding of data privacy, encryption, and AI security

Familiarity with modern APIs, microservices, and CI/CD pipelines

Nice to Have:

Experience in finance, healthcare, legal, or other regulated domains

Knowledge of AI ethics frameworks (EU AI Act, NIST AI Risk)

MLOps / AIOps / AI observability tooling

Certifications in AI governance, cloud security, or compliance, AI .Net Azure

Competitive salary

30 days' annual leave (increasing to 35 over time)

Employer pension contributions

Employee benefits programme (after probation)

Hybrid working model with flexibility

Training budget for professional development

This is a unique opportunity to shape enterprise AI strategy in a fast-growing, forward-thinking company. You'll take ownership of building solutions that are both technically robust and business-critical.

Reach out to Ryan Quinn on LinkedIn for more info, or apply directly with your CV.

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