Enterprise Architect

Roles and Responsibilities : Essential Skills/Experience - Enterprise Architecture experience: proven EA leadership translating concepts into production-ready solutions. - Hands-on AI/ML engineering: building, fine-tuning, and deploying ML/DL models (LLMs, RAG, MLOps) in production. - AI platforms: hands-on with AWS (Bedrock, SageMaker, Amazon Q), Azure (Azure AI, ML, OpenAI), and Databricks. - Engineering and analytics: strong Python, TensorFlow/PyTorch, containers, Kubernetes, and CI/CD for hybrid cloud. - Data modelling and governance: conceptual/logical modelling and governance standards in regulated environments. - Architecture judgement: select fit-for-purpose AI architecture per use case, with full-lifecycle understanding. - Leadership: lead a small team of AI architects and help shape enterprise AI strategy and direction. - Degree in data science, AI engineering, or a related field (or equivalent experience). Desirable Skills/Experience - Postgraduate degree in MIS, AI, data science, or a related field. - Recognised thought leader in applying AI within the enterprise and across the industry. - Extensive senior AI, data science, data engineering, and AI architecture experience delivering large-scale blueprints. - Hands-on building AI models, including LLMs and LVMs, across diverse data types. - Agile AI delivery experience; tools for metadata cataloguing, data modelling, and enterprise architecture. - Experience in the pharmaceutical AI industry. - Ability to simplify the enterprise landscape and reduce technology debt.