Senior AI Engineer

Competitive Salary, Plus Benefits

Charterhouse is partnered with a fast-growing technology company at the forefront of AI-optimised hardware and infrastructure. This business is building the infrastructure that powers the next generation of AI. As their team expands, they are looking to hire Senior AI Engineers to join a highly technical and driven engineering team in Dubai.

The Senior AI Engineer will take ownership of designing and building scalable AI systems and pipelines centred around LLMs, leveraging Python and modern GenAI frameworks. A key part of the role involves developing internal RAG-based systems and compelling demo applications that demonstrate the capabilities of the company's proprietary hardware. The position also requires close collaboration with highly technical engineering teams to profile and optimise AI workloads across diverse hardware platforms, including custom accelerator architectures.

On the technical delivery side, the Senior AI Engineer will be responsible for optimising prompts, embeddings, retrieval strategies, and overall model behaviour for production-grade performance, as well as fine-tuning models for domain-specific use cases using approaches such as LoRA, qLoRA, or full fine-tuning. The role involves building and maintaining custom pipelines for document ingestion, chunking, embedding generation, and retrieval, while defining robust workflows for model versioning, reproducibility, and experiment tracking.

The ideal candidate will bring strong Python programming skills and hands-on experience with data analysis libraries including Pandas, NumPy, and SQL. A proven track record delivering retrieval-augmented generation (RAG) solutions is essential, alongside experience working with vector databases such as Milvus or Chroma, and evaluation frameworks such as Ragas or DeepEval. Experience deploying and monitoring large-scale AI systems using tools such as ClearML or Kubeflow is highly advantageous.

Given the nature of the client's business, hands-on experience deploying and optimising AI models on GPU, NPU, TPU, or custom ASIC hardware is a strong requirement for this role. Candidates who have worked directly with AI accelerator infrastructure and understand low-level performance implications will be prioritised.