Key Responsibilities Responsible for leading the development and implementation of generative AI solutions within the Group Data & AI division. The Head of GenAI Development will be responsible for defining the bank&aposs GenAI strategy, overseeing the development of AI systems and architectures, and ensuring the successful delivery of Gen AI solutions that drive innovation and create value for the bank. AI Strategy Development: Define the bank&aposs GenAI strategy, aligning it with the overall business objectives and technology roadmap. Identify and evaluate potential applications of GenAI across the bank&aposs various business lines. Assess the risks and benefits associated with adopting Gen AI technologies. Develop a roadmap for GenAI development and implementation, including timelines, resource allocation, and key milestones. Collaborate with senior leadership to secure funding and support for Gen AI initiatives. Stay up-to-date with the latest trends and advancements in GenAI. AI System Architecture: Design and implement scalable and efficient AI system architectures that support GenAI applications. Select and integrate appropriate hardware and software components for GenAI development. Ensure the security and privacy of AI systems and data. Develop guidelines and standards for AI development and deployment. Collaborate with data engineers and architects to build robust data pipelines and infrastructure. Solutioning: Lead the development of GenAI solutions for specific business problems, such as customer service, fraud detection, and product recommendations. Collaborate with data scientists and domain experts to define problem statements and identify potential solutions. Select and apply appropriate GenAI techniques, such as natural language processing, image generation, and recommendation systems. Evaluate the performance and effectiveness of GenAI solutions. Iterate on GenAI models and algorithms to improve accuracy and efficiency. Model Development and Deployment: Lead the development and deployment of high-quality generative AI models, including natural language processing (NLP), computer vision, and other relevant techniques. Ensure the accuracy, reliability, and scalability of generative AI models, and continuously monitor and improve their performance. Collaborate with IT and operations teams to integrate generative AI models into existing systems and infrastructure. Develop and implement robust data governance and privacy policies to protect sensitive customer data Qualification Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field. Technical/Functional Skills: Proficiency in programming languages such as Python, R, or Java, and experience with deep learning frameworks (e.g., TensorFlow, PyTorch). Strong knowledge of NLP, computer vision, and other generative AI techniques (e.g., GPT, BERT, GANs). Practical experience with: - Python programming - LLM frameworks (e.g., LangChain, LlamaIndex) - Vector databases (e.g., Pinecone, Weaviate) - Prompt engineering techniques Experience with MLOps tools and practices, including version control, CI/CD, containerization (e.g., Docker), and orchestration (e.g., Kubernetes). Strong understanding of data and AI technologies, trends, and best practices. Experience in working with data-driven projects and initiatives. Computer Literacy - knowledge in Microsoft Office Excel, Words, & Powerpoint Personal skills (Soft Competencies [Core/Leadership]): Ability to manage AI/ML projects, including model development, testing, deployment, and monitoring. Strong understanding of agile methodologies and experience working in cross-functional teams. Excellent analytical, problem-solving, and communication skills. Ability to work collaboratively with technical and non-technical stakeholders. Proactive mindset with a strong drive for innovation and continuous learning. Show more Show less