AI Lead

About The Role We're looking for a hands-on AI Lead to join one of our clients, a growing technology company, and lead its AI strategy and delivery. You'll manage and mentor a small team of engineers and data scientists, working closely with business and technology stakeholders to turn business needs into practical AI solutions with measurable results. Alongside leading the team, you'll contribute to solution architecture, development and technical reviews, ensuring AI applications are reliable, secure and cost-effective. What you'll do • Define the AI strategy, roadmap and priorities with business and technology leadership, aligning initiatives with business objectives. • Lead and mentor ML engineers and data scientists, setting clear goals, reviewing technical work and supporting team development. • Assess business problems and prioritise AI use cases based on value, feasibility, data readiness and implementation effort. • Own end-to-end AI delivery, from discovery and experimentation through development, deployment and ongoing monitoring. • Contribute directly to solution architecture, development and code reviews, establishing clear engineering and documentation standards. • Build applications using LLMs, retrieval-augmented generation (RAG) and generative AI, selecting traditional machine learning or simpler approaches where appropriate. • Collaborate with data engineering teams to ensure reliable data pipelines and suitable data quality for training, evaluation and production use. • Establish MLOps practices for experiment tracking, versioning, testing, deployment, monitoring and retraining where needed. • Define evaluation criteria and track model quality, business impact, latency and operating costs. • Evaluate generative AI applications for accuracy, hallucinations, safety and reliability before release and throughout production use. • Partner with legal, security and compliance teams to address applicable PDPL, data governance and data residency requirements. • Evaluate vendors, tools and platforms, making clear build-versus-buy recommendations. • Communicate progress, results, risks and investment needs to senior stakeholders in clear business terms. What you bring • 7+ years of experience in data science, machine learning or AI engineering, including at least 3 years managing and mentoring technical teams. • A proven track record of deploying and maintaining AI solutions in production, beyond proofs of concept. • Strong Python programming skills and experience with frameworks such as PyTorch, TensorFlow or scikit-learn. • Hands-on experience building LLM applications, including RAG, prompt engineering and evaluation methods. • Experience with vector search technologies such as Pinecone, Weaviate, Milvus or pgvector. • Experience designing AI architectures and integrating models into business applications through APIs and services. • Experience with at least one major cloud platform: AWS, Azure or GCP. • Practical experience with MLOps tools or platforms such as MLflow, Kubeflow, Amazon SageMaker or Azure Machine Learning. • Familiarity with Docker, Git and CI/CD pipelines for testing, packaging and deploying AI applications. • Strong understanding of data quality, model evaluation and the trade-offs between performance, reliability, cost and speed. • Ability to translate business needs into technical priorities and make informed delivery decisions. • Awareness of privacy, security and responsible AI considerations across the development lifecycle. • Strong stakeholder management and communication skills in English. Arabic is an advantage. Nice to have • Familiarity with LLM application frameworks such as LangChain or LlamaIndex, and tools for evaluating and monitoring LLM applications. • Experience with Hugging Face Transformers and adapting pretrained models to specific use cases. • Experience deploying and managing AI workloads on Kubernetes. • Experience with Arabic NLP or Arabic-language LLM applications. • Experience delivering AI solutions in banking, fintech, government or telecom in the GCC. • Familiarity with SDAIA guidance and responsible AI frameworks. • A master's degree in AI, Machine Learning, Data Science or a related field.

7+ years of experience in data science, machine learning or AI engineering, including at least 3 years managing and mentoring technical teams. A proven track record of deploying and maintaining AI solutions in production, beyond proofs of concept.

Define the AI strategy, roadmap and priorities with business and technology leadership, aligning initiatives with business objectives. Lead and mentor ML engineers and data scientists, setting clear goals, reviewing technical work and supporting team development. Assess business problems and prioritise AI use cases based on value, feasibility, data readiness and implementation effort. Own end-to-end AI delivery, from discovery and experimentation through development, deployment and ongoing monitoring. Contribute directly to solution architecture, development and code reviews, establishing clear engineering and documentation standards. Build applications using LLMs, retrieval-augmented generation (RAG) and generative AI, selecting traditional machine learning or simpler approaches where appropriate. Collaborate with data engineering teams to ensure reliable data pipelines and suitable data quality for training, evaluation and production use. Establish MLOps practices for experiment tracking, versioning, testing, deployment, monitoring and retraining where needed. Define evaluation criteria and track model quality, business impact, latency and operating costs. Evaluate generative AI applications for accuracy, hallucinations, safety and reliability before release and throughout production use. Partner with legal, security and compliance teams to address applicable PDPL, data governance and data residency requirements. Evaluate vendors, tools and platforms, making clear build-versus-buy recommendations. Communicate progress, results, risks and investment needs to senior stakeholders in clear business terms.