We are seeking a highly skilled Senior AI Engineer 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. We are looking for a UAE National and the ideal candidate will bring deep expertise across the AI stack, from model architecture to deployment, and thrive 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: - UAE National - 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