Job details
| Location: | Dubai |
| Salary: | Competitive Salary |
| Job Type: | Permanent |
| Discipline: | |
| Reference: | 53159 |
| Posted: | about 10 hours ago |
Job description
Charterhouse is partnering with a fast-growing technology company pioneering the development of advanced server hardware tailored for artificial intelligence (AI) and machine learning (ML) workloads. Our Client is dedicated to accelerating AI innovation by delivering high-performance, scalable, and energy-efficient infrastructure solutions. As part of their expansion, they are looking to hire experienced AI Engineers (multiple openings) to join their team.
About the role
The AI Engineer will be responsible for designing and implementing scalable systems based on large language models, leveraging Python and modern generative AI frameworks. In this role, the Engineer will contribute to the development of internal GenAI systems and demo applications that showcase the capabilities of the company’s proprietary hardware.
Key responsibilities include optimizing prompts, embeddings, retrieval mechanisms, and model behaviour, as well as fine-tuning models to enhance domain-specific performance. The role requires building custom pipelines for document ingestion, chunking, embedding generation, and retrieval, in addition to maintaining workflows for model versioning, reproducibility, as well as experiment tracking. Furthermore, the AI Engineer will also collaborate with internal and external teams to define evaluation workflows for custom AI hardware and frameworks, and to profile AI workloads across diverse platforms.
About you
The successful candidate will demonstrate strong programming skills in Python and familiarity with data analysis libraries such as Pandas, NumPy, and SQL which are essential for the role. Hands-on experience deploying retrieval-augmented generation (RAG) solutions, working with vector databases (e.g., Milvus, Chroma), and utilizing evaluation frameworks such as Ragas or DeepEval is required.
In addition, experience with large-scale deployment and monitoring tools (e.g., ClearML, Kubeflow) is also highly desirable, along with a solid understanding of software engineering best practices, including testing, debugging, documentation, and version control. Knowledge of CPU, GPU, or custom accelerator architectures such as NPUs, TPUs is preferred, but not mandatory.
