AI Engineer
About the job
We have partnered with a leading technology-driven business in the UAE to hire an AI Engineer. The UAE is one of the most active AI hiring markets in the world right now, and the best engineers here have their pick of opportunities across fintech, conglomerates, government-backed ventures and high-growth startups. This role sits within an organisation that is serious about AI, with the infrastructure, leadership and ambition to match. If you are weighing up your options in this market, this one is worth your attention.
This is a hands-on engineering role for someone who builds things that ship. You will work on real AI systems in production, not prototypes that live in a slide deck.
About the role:
Design, build and deploy production-grade AI and ML systems across a range of business use cases, owning delivery from concept through to production
Develop and maintain LLM-based applications including RAG pipelines, agentic workflows and tool-use systems
Build and integrate APIs, orchestration layers and data pipelines that connect AI components into broader platform architecture
Work with vector databases, embedding models and retrieval frameworks to build accurate, scalable AI solutions
Ensure deployed systems meet production standards across performance, reliability, observability and cost efficiency
Collaborate with product, data and engineering stakeholders to translate business problems into well-architected AI solutions
Stay current with emerging AI techniques and contribute new thinking to the team
About you:
3 to 5 years of hands-on experience in AI or ML engineering, with a clear track record of deploying systems into production
Strong Python skills and practical experience with modern AI frameworks including TensorFlow, PyTorch or Hugging Face
Hands-on experience with LLM APIs, RAG system design and vector databases such as Pinecone, Weaviate or pgvector
Familiarity with orchestration frameworks such as LangChain or LlamaIndex and agentic system patterns
Solid understanding of cloud platforms including AWS, Azure or GCP and how AI systems are deployed and maintained at scale