Enterprise Artificial Intelligence Architect – GEA IT

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 global 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 requirements, seek to collaborate effectively with data scientists, data engineers, data analysts, ML engineers, other architects, business unit leaders and CxOs (technical and nontechnical personnel), and harmonize the relationships among them. 

EDO/IT aligned Accountabilities: 

• Deliver and manage Information Architecture (IA), conceptual and logical models, for operational, master and data products including data models, information flows, master/reference data and metadata designs to meet one or more Business Capability requirements.   

• Work with business leaders to evolve IA designs to support business strategy. 

• Own the IA designs of one or more Business capability level IA designs, especially major/complex Business Capability areas. 

• Take on accountability for the IA of major change programs impacting whole Business capability areas.  

• Assure IT change projects in that Business Capability are aligned to IA through either delivering IA blueprint artefacts or assessing designs from AI Models /Solution Architects assigned to the project. 

• Partner with the Data Offices to ensure data governance processes are enabled in all IA designs and provide assurance evidence through standardized metrics: e.g., Master data consumption, data classification metadata application to support access processes. 

• Responsible for selecting or defining the correct architecture and patterns to fulfil traditional reporting (management information, business intelligence) and analytics, data science, digital, and operational business use cases. This includes: 

• Providing both strategic and tactical data architecture planning, design expertise and execution application on development projects, from technical designs and technology standards to Models and IA considerations. 

• Providing both strategic and tactical coherent architectural thinking in support of data and other strategies, enterprise data and information architectures. 

• Supports (or leads) in the development of AI Integration architecture and overall Data Integration design of a project. 

• Gains approval for the various IA artifacts ensuring enforcement of standard enterprise data element names, abbreviations, characteristics, and domains during the lifecycle of a project. 

• Specify and manage work packages for FTE’s and flexible resources. 

• Demand Management and Recharging related to AI /IT projects/programmes, working in conjunction with the EIA Practice & Project Manager and EIA LT, alongside AI Project Managers, Business Partners etc 

Essential Skills/Experience

• Bachelor’s Degree or equivalent number of years of experience in a Data Science or AI engineering or related field. 

• Experience in leading and delivering enterprise AI platform architectural thinking, and its practical application. 

• Experience in the use of conceptual and logical data modelling technologies. 

• Experience in defining and working with information and data regulatory governances. 

• The role holder will possess a blend of data/information architecture, analysis, and engineering skills. 

• Experience in known industry IT architectural patterns and IT architecture ways of working/methodologies (e.g. Amazon Bedrock, Amazon Q and Sagemaker). 

• Understands AI Platforms concepts and cloud-based containerization strategies for hybrid cloud environments. 

• Understanding the appropriate AI structure and technology based on business use case and completely familiar with AI lifecycles. 

• Manage small group of talent AI Architect and drive AI strategy and direction for Enterprise Landscape.

Desirable Skills/Experience

• Post-graduate degree in MIS, AI  

• Extensive experience in a senior AI /data Science and Data Engineering and AI architecture role with practical examples of designing and providing end-to-end and point data architecture designs or blueprints that have been delivered and implemented for substantial real-world use cases. 

• Experience of AI and Data Governance frameworks and their application in a commercial organisation 

• Experience in Agile AI definition scrums. 

• Experience in the use of tooling, e.g. metadata cataloguing tools, data modelling tools, EA tools  

• Hands on experience in AI model, building LLM and LVM working closely with different data type and generating clear accuracy models.  

• Experience of working in the AI Pharmaceutical industry. 

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

At AstraZeneca, we are supported with all the tools of a leading pharmaceutical company coupled with an agility mentality. We are quick to adapt and evolve in our dynamic fields of medicine, science, and technology. Our work is about disrupting the status quo and thinking critically to drive innovation. We embrace change and naturally adapt to our ever-evolving industry. Our inclusive approach celebrates different perspectives which drive our innovation. We are proud to be part of a value-driven business built on a collaborative culture that supports your ambitions and learning at scale.

Ready to make an impact? Apply now to join our journey!

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.