A leading organisation in the UAE is seeking a highly skilled Senior Generative AI Engineer to design, optimise, and deploy large-scale AI solutions at the enterprise level. This is a technically demanding, full-time role requiring 3+ years of experience in LLM development, optimisation, and AI deployment — with deep expertise in LLaMA, GPT-4, Falcon, PyTorch, CUDA, TensorRT, FAISS, Pinecone, LangChain, and cloud AI platforms including AWS, Azure, and GCP. If you are a senior AI engineer with hands-on production LLM experience, GPU optimisation capability, and the enterprise AI architecture depth to deliver at scale, this is one of the most compelling Generative AI roles available in the UAE in 2026.
About the Role — Enterprise-Scale Generative AI in the UAE
Sector: Enterprise AI — generative models, NLP applications, and cloud-deployed AI infrastructure
Scale: Production-grade, large-scale AI deployments — optimised for cost, performance, and scalability
Models: LLaMA, GPT-4, Falcon — advanced architectures with fine-tuning and compression
Research: Active contribution to LLM lifecycle management, reinforcement learning, and adaptive AI
Innovation: Stay ahead of AI trends — LangChain, Auto-GPT, DeepSpeed orchestration applied in production
Core Technology Stack
LLM Frameworks: LLaMA, GPT-4, Falcon — LoRA, quantization, PEFT for efficient fine-tuning
GPU Compute: CUDA, TensorRT, ONNX Runtime, PyTorch — GPU-accelerated training and inference
Vector Databases: FAISS, Pinecone, Milvus, ChromaDB — embedding pipelines and semantic search
Cloud Platforms: AWS SageMaker, Azure AI, Google AI Platform — end-to-end AI pipeline deployment
Orchestration: LangChain, Auto-GPT, DeepSpeed, Weights & Biases (WandB) — MLOps and lifecycle
Position Overview
The Senior Generative AI Engineer in the UAE is a technically expert, enterprise-focused AI engineering role at the frontier of Large Language Model development and deployment. You will architect and optimise LLM-based generative AI models, implement advanced model compression techniques for production efficiency, develop NLP solutions for real-world enterprise applications, and build vector database pipelines that power high-performance semantic search and AI memory systems — all within a GPU-accelerated, cloud-deployed infrastructure built for scale.
This role demands the rare combination of deep LLM architecture expertise, hands-on GPU optimisation capability, NLP application development skill, and cloud AI deployment experience that separates senior AI engineers who can operate in production environments from those who can only prototype in research settings. You will be expected to research and implement the latest advancements in AI — from reinforcement learning and adaptive frameworks to DeepSpeed and LangChain orchestration — and contribute meaningfully to the organisation’s long-term AI capability and competitive differentiation in the UAE’s rapidly growing enterprise AI market.
Key Responsibilities
Advanced AI Model Development & Optimisation
- Architect and optimise LLM-based generative AI models including LLaMA, GPT-4, and Falcon — applying deep knowledge of transformer architectures, attention mechanisms, and model behaviour to design systems that perform reliably at enterprise scale in production UAE deployments
- Implement advanced model compression techniques including LoRA (Low-Rank Adaptation), quantization, and PEFT (Parameter-Efficient Fine-Tuning) — reducing model size and inference cost without sacrificing the accuracy and quality required by enterprise AI applications
- Utilise GPU-accelerated frameworks including CUDA, TensorRT, and PyTorch for optimised model training and inference — applying hardware-aware engineering to maximise GPU utilisation, minimise latency, and deliver the throughput required by high-volume production AI workloads
- Lead LLM fine-tuning workflows — designing and executing domain-specific fine-tuning pipelines that adapt large pre-trained language models to the specific vocabulary, tone, and knowledge requirements of enterprise AI applications in the UAE market
NLP Applications & AI Solutions Development
- Develop NLP solutions for chatbots, summarisation, semantic search, and contextual understanding — building production-grade natural language processing applications that address real enterprise use cases with the accuracy, reliability, and response quality that business users require
- Implement intent recognition, entity extraction, and sentiment analysis systems — engineering the NLP pipeline components that enable intelligent, context-aware AI applications to understand user inputs accurately and respond with genuine semantic comprehension
- Design and build AI-driven automation workflows — identifying and implementing opportunities to automate complex, language-intensive business processes through LLM-powered orchestration that reduces manual effort and improves operational consistency at scale
- Develop and optimise embedding generation pipelines — producing high-quality vector representations of text, documents, and knowledge bases that support accurate semantic search, retrieval-augmented generation (RAG), and AI memory applications
Vector Databases & AI Memory Systems
- Work with FAISS, Pinecone, Milvus, and ChromaDB for high-performance vector search — designing and optimising vector database architectures that enable fast, accurate similarity search across large-scale enterprise knowledge bases for RAG and semantic retrieval applications
- Develop real-time AI application pipelines — engineering the complete data flow from document ingestion and chunking through embedding generation, vector indexing, query processing, and LLM response generation, with the latency and reliability requirements of live enterprise applications
- Implement AI memory and context management systems — designing the retrieval and persistence layers that allow AI applications to maintain context across extended interactions and access relevant historical knowledge for more accurate, personalised responses
GPU Optimisation & Large-Scale Cloud Deployment
- Optimise LLM performance using TensorRT, ONNX Runtime, and multi-GPU parallelisation — implementing the hardware-software co-optimisation strategies that allow large models to run efficiently across distributed GPU infrastructure at the cost-performance ratios production environments demand
- Deploy AI models on AWS SageMaker, Azure AI, or Google AI Platform — designing cloud-native AI deployment architectures that are scalable, cost-efficient, highly available, and aligned with the enterprise security and compliance requirements of UAE business environments
- Implement APIs, microservices, and end-to-end AI pipelines — engineering the complete integration layer that connects LLM capabilities to enterprise applications, data systems, and business workflows through well-designed, reliable, and maintainable software interfaces
- Stay at the forefront of AI tooling — implementing LangChain, Auto-GPT, and DeepSpeed for advanced AI orchestration, and contributing to LLM lifecycle management, reinforcement learning, and adaptive AI framework development as the field continues to evolve
Required Skills & Qualifications
Educational Requirements
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a closely related quantitative field — with strong foundations in mathematics, statistics, linear algebra, and the theoretical underpinnings of modern deep learning and generative AI systems
Core Technical Requirements
- 3+ years of hands-on experience in LLM development, optimisation, and AI deployment — with demonstrable contributions to production AI systems using large language models, not just research or academic exploration of these technologies
- Strong expertise in Python, PyTorch, TensorFlow, and Hugging Face Transformers — with the engineering rigour to produce clean, testable, production-quality AI code that meets professional software engineering standards
- Hands-on experience with Meta’s LLaMA, GPT-4, Falcon, or similar LLM architectures — with deep understanding of their capabilities, limitations, and the fine-tuning and optimisation approaches that make them production-ready for specific enterprise applications
- Deep understanding of GPU-based AI development including CUDA, TensorRT, and ONNX Runtime — able to diagnose GPU utilisation bottlenecks, implement kernel-level optimisations, and apply multi-GPU parallelisation strategies for high-throughput AI inference
Preferred Skills
- Experience with LangChain, Auto-GPT, and DeepSpeed for AI pipeline orchestration — demonstrating familiarity with the agentic AI frameworks and distributed training tools that are becoming standard in advanced enterprise AI deployments
- Knowledge of MLOps and LLM lifecycle management — including model versioning, experiment tracking, deployment automation, and production monitoring practices that ensure AI systems remain reliable and improvable over time
- Familiarity with Weights & Biases (WandB) for AI experimentation tracking — enabling systematic comparison of training runs, hyperparameter configurations, and model versions across the LLM development lifecycle
Why Senior Generative AI Engineer Roles in the UAE Are Exceptional in 2026
The UAE has positioned itself as a global leader in AI adoption and investment — with the UAE National AI Strategy 2031, sovereign AI infrastructure buildouts, and private sector AI transformation initiatives creating one of the world’s most active demand environments for senior Generative AI engineers who can operate at the enterprise level. The combination of national strategic intent, capital availability, and a rapidly modernising enterprise technology landscape means that the LLM engineers who build AI systems in the UAE in 2026 are working on problems that genuinely matter at a national scale.
For a Senior Generative AI Engineer with the LLM architecture depth, GPU optimisation expertise, vector search capability, and cloud deployment experience this role demands, the UAE offers a uniquely compelling combination in 2026: zero personal income tax, competitive senior AI compensation, cutting-edge enterprise AI challenges, and the professional significance of contributing to AI systems in a market that is actively investing in becoming a global AI hub. If your LLM engineering capability is genuinely production-grade, this is where it belongs.
Who Should Apply?
- Senior LLM Engineers with Production Experience: With 3+ years of hands-on LLM development and deployment — working with LLaMA, GPT-4, or Falcon in real enterprise environments, not just research prototyping
- GPU AI Optimisation Engineers: With deep CUDA, TensorRT, and multi-GPU parallelisation expertise — able to diagnose performance bottlenecks and implement hardware-aware optimisations that make large models cost-efficient at production scale
- Vector Search & RAG Specialists: With hands-on experience in FAISS, Pinecone, Milvus, or ChromaDB — building embedding pipelines and retrieval-augmented generation architectures that power enterprise AI memory and semantic search applications
- Cloud AI Deployment Engineers: With strong AWS SageMaker, Azure AI, or GCP AI Platform deployment experience — able to design scalable, secure, and cost-optimised cloud AI infrastructure for enterprise production environments
- UAE-Based or Relocating Senior AI Engineers: Seeking a full-time Generative AI role in one of the world’s most active AI investment regions — contributing to enterprise AI systems at the frontier of LLM technology within the UAE’s rapidly expanding AI ecosystem
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