Our client is an international organisation operating within the professional training, events and knowledge sectors. They work with businesses and professionals across a range of industries, providing specialist learning programmes, insights and networking opportunities that help organisations develop capabilities and improve performance.

With a global presence and teams working across multiple regions, the organisation delivers a wide portfolio of training solutions, including both in-person and virtual programmes.

The company works closely with client organisations to identify their business and training requirements, collaborating with subject matter experts to develop tailored learning programmes and solutions.

We are seeking a highly skilled Senior AI Engineer [UAEN] to lead the strategy, design, deployment, and optimization of advanced AI systems. This is a hands-on, technical leadership role responsible for driving machine learning (ML), computer vision, large language model (LLM), and MLOps initiatives. The ideal candidate brings deep expertise across the AI stack—from model architecture to deployment—and thrives in a collaborative, cross-functional environment.

Key Responsibilities

• Design, develop, and train advanced computer vision models including CNNs, Vision Transformers, and diffusion models for tasks such as image/video understanding, segmentation, and generation.
• Architect and implement on-premise large language models (LLMs) using platforms like Ollama, OpenWebUI, and PrivateGPT. This includes fine-tuning, quantization, and secure deployment.
• Optimize LLM pipelines for tasks like translation, summarization, semantic search, and prompt-based reasoning, ensuring performance, latency, and cost efficiency.
• Build and productionize retrieval-augmented generation (RAG) systems using frameworks such as LangChain and vector databases (e.g., Pinecone, FAISS, Milvus).
• Establish and maintain robust MLOps practices, including containerization (Docker), orchestration (Kubernetes), CI/CD pipelines, monitoring, and automated retraining.
• Develop and maintain REST and GraphQL APIs to serve ML models, manage inference workflows, and integrate with end-user applications.
• Lead edge AI deployments on platforms such as NVIDIA Jetson, focusing on performance-optimized, low-latency inference at the edge.
• Drive training and inference performance improvements using distributed training, mixed-precision computing, model parallelism, and tools like TensorRT, ONNX Runtime, and DeepSpeed.
• Collaborate with stakeholders—product managers, engineers, QA, and operations teams—to translate requirements into AI-driven solutions.
• Mentor junior engineers and promote best practices in experiment tracking, reproducibility, and knowledge sharing.
• Create technical documentation, proof-of-concept prototypes, performance benchmarks, and cost analyses to support decision-making.

Required Technical Skills and Experience

• Bachelor's or Master’s degree in Computer Science, Engineering, AI/ML, or related field (PhD preferred).
• Minimum 5 years of experience in building and deploying computer vision models using TensorFlow and PyTorch.
• Experience with on-prem LLM hosting and customization (Ollama, PrivateGPT, OpenWebUI).
• Strong understanding of LLM applications including summarization, translation, prompt engineering, and semantic search.
• Experience in RAG system development, vector databases, and embedding techniques.
• Solid MLOps background: Docker, Kubernetes, CI/CD, model monitoring (Prometheus, Grafana), and automation.
• Strong proficiency in Python and relevant ML libraries.
• Experience deploying models to edge devices (e.g., Jetson, ARM).
• Expertise in model optimization techniques (quantization, pruning, mixed precision) and performance tuning.
• Familiarity with cloud and hybrid infrastructure (AWS, Azure, GCP).

Desirable Qualifications

• Experience in regulated or security-sensitive environments with knowledge of data privacy, secure AI deployment, and governance.
• Familiarity with tools for model monitoring, drift detection, and explainability.
• Background in AI R&D or productization of machine learning systems in industry.

Key Competencies

• Strategic thinking and hands-on execution
• Technical leadership and team mentorship
• Strong cross-functional communication
• Problem-solving and delivery under tight timelines
• Commitment to high-quality, reproducible AI development

Performance Metrics

• Time to production for POCs and models
• SLA-based model performance (accuracy, latency, uptime)
• Inference cost reduction and throughput gains
• Successful documentation and deployment handover
• Edge rollout success and reliability

Working Conditions and Benefits

• Fast-paced, multidisciplinary R&D environment
• Competitive compensation and benefits
• Professional development allowance
• Opportunities to lead and shape strategic AI initiatives

Thank you for your application.  This role is for UAE Nationals only. Only suitable candidates will be contacted.

Please note that by applying to this role, your profile will be searchable in our database.  Our Recruiters will contact you should another suitable role become available.

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