**Introduction to role**Our Global Enterprise Architect (GEA) Team supports the global development of AstraZeneca AI products, including Amazon Q, Sagemaker, Amazon Bedrock, OpenAI, GCP, Azure and various technologies like Landing AI and DataBrick. Additionally, we are involved in data security, data platforms, and analytical solutions. We collaborate closely with teams across the Business Technology Group (BTG - Business Unit) IT teams, research and development, data governance teams, and SET Area Data Offices (SEDO).We lead the integration of large language models (LLMs) and LangChain into business processes. Our team utilizes Python and other data manipulation languages proficiently to prepare and manipulate data. We understand business requirements and translate them into Gen AI solution designs that successfully meet business objectives.Furthermore, the GEA considers enterprise data architectures with a focus on enterprise information architecture, data modelling, and data analysis. Our worldwide enterprise AI, data, Integration architecture (EAIA) practice operates within the GEA. We provide critical designs, patterns, reference architecture, frameworks, and services focused on ingestion, extraction, processing, transformation, transport, storage, data visualization, data security, and representation of knowledge, as well as analysis and modelling of crucial data and facts.We design the fundamental elements of the data world for AstraZeneca across our general customer businesses. We employ cutting-edge procedures with a focus on enterprise data architecture, data modelling, data analysis, data governance, data integration, and data security. Our worldwide enterprise AI architect (EAIA) practice operates within the GEA, providing essential services centred on new pharmaceutical innovation and drug discovery, as well as the analysis and modelling of significant AI data and facts.We develop the core components of the AI landscape for AstraZeneca across our international customer base. Leading enterprise-level, cross-organizational data architecture includes defining and delivering AI products, aligning with the AI platform, and adhering to FAIR AI principles across our businesses. We employ leading methodologies and processes to achieve sustainable outcomes for projects and ongoing operations.**Accountabilities****Enterprise Information Architecture Accountabilities**:- Collaborate with data scientists and other AI professionals to augment digital transformation efforts by identifying and piloting use cases. Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation. At the same time, bring attention to misaligned initiatives and impractical use cases.- Align technical implementation with existing and future requirements by gathering inputs from multiple stakeholders — business users, data scientists, security professionals, data engineers and analysts, and those in IT operations — and developing processes and products based on the inputs.- Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings. Select cloud, on-premises, or hybrid deployment models, and ensure new tools are well-integrated with existing data management and analytics tools.- Audit AI tools and practices across data, models, and software engineering with a focus on continuous improvement. Ensure a feedback mechanism to assess AI services, support model recalibration and retrain models.- Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model theft and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. Remain acquainted with upcoming regulations and map them to best practices.- AI architecture and pipeline planning. Understand the workflow and pipeline architectures of ML and deep learning workloads. An in-depth knowledge of components and architectural trade-offs involved across the data management, governance, model building, deployment and production workflows of AI is a must.- Software engineering and DevOps principles, including knowledge of DevOps workflows and tools, such as Git, containers, Kubernetes and CI/CD.- Data science and advanced analytics, including knowledge of advanced analytics tools (such as SAS, R and Python) along with applied mathematics, ML and Deep Learning frameworks (such as TensorFlow) and ML techniques (such as random forest and neural networks).- Thought leadership. Be change agents to help the organization adopt an AI-driven mindset. Take a pragmatic approach to the limitations and risks of AI, and project a realistic picture in front of IT executives who provide overall digital thought leadership.- Collaborative mindset. To ensure that AI platforms deliver both business and technical requ