Artificial Intelligence Specialist

Role Summary Senior Data Consulting Lead with deep expertise in Data Architecture, Data Products, and AI-led Platforms, specialising in Insurance (with focus on Specialty Lines). This role drives enterprise-scale data and AI transformation, shaping modern data ecosystems, AI platforms, and AI-driven migration strategies on Azure, Databricks, and Power BI. A recognised thought leader, responsible for influencing C-level stakeholders, defining strategy, and delivering measurable outcomes through data + AI convergence. Key Responsibilities 1. Data Strategy, AI Vision & Thought Leadership • Define enterprise-wide data and AI strategy aligned to business and regulatory priorities • Act as a trusted advisor to CIO/CDO/AI leadership, shaping data & AI transformation roadmaps • Drive data product thinking with embedded AI/ML capabilities (intelligent underwriting, claims automation, pricing optimisation) • Bring market perspective on AI-native data ecosystems, GenAI enablement, and agentic architectures 1. Data & AI Architecture Leadership • Own end-to-end architecture across data and AI layers: • Data ingestion, processing, modelling, semantic layer, and consumption • AI platform integration (model lifecycle, feature engineering, inference pipelines) • Design modern Lakehouse + AI architecture leveraging Azure and Databricks • Define architecture for scalable, governed, and reusable AI-ready data platforms • Ensure integration of data governance, lineage, security, and responsible AI principles 1. AI Platforms & AI-led Data Migration • Design and implement AI Platforms integrating: • Model development environments, MLOps pipelines, feature stores, and model serving • Lead AI-driven migration strategies, including: • Automated schema discovery, data mapping, and transformation using AI accelerators • AI-assisted code conversion (e.g., legacy ETL → modern pipelines) • Intelligent data quality assessment and anomaly detection • Drive adoption of AI-enabled accelerators to: • Reduce migration timelines • Improve accuracy and minimise manual intervention • Enable continuous intelligence through pipelines that combine data engineering with AI/ML workflows 1. Insurance Domain & Data Products • Deep understanding of Specialty Lines insurance (Commercial, Marine, Liability, etc.) • Define and operationalise domain-centric data products, such as: • Risk profiling and underwriting intelligence • Claims analytics and fraud detection models • Pricing optimisation models • Customer and broker analytics platforms • Align data products to business outcomes, regulatory compliance, and monetisation opportunities 1. Technology Leadership (Azure + Databricks + Power BI) • Lead architecture and execution of: • Azure Data Platform (ADF, Synapse, Fabric, ADLS) • Databricks (Lakehouse, Delta, ML workflows, PySpark pipelines) • Power BI (semantic models, enterprise dashboards, self-service BI) • Drive adoption of: • Metadata-driven architectures • Automation, orchestration, and reusable frameworks • Ensure separation and optimisation of data engineering, analytics, and AI workloads 1. Consulting & Delivery Leadership • Lead end-to-end consulting engagements (Discovery → Architecture → Delivery → Value Realisation) • Run executive workshops on Data Strategy, AI adoption, and operating models • Define target operating models (Data + AI CoE, Data Product organisation) • Mentor teams across architecture, engineering, analytics, and AI • Build reusable accelerators and GTM offerings in data + AI transformation Required Experience & Skills Core Experience • 12–18+ years across Data, Analytics, AI Platforms, and Architecture • Proven leadership of large-scale data and AI transformation programmes • Strong experience in consulting, stakeholder engagement, and solution shaping Insurance Expertis • eStrong domain expertise in Insurance (with exposure to Specialty Lines • )Understanding of underwriting, claims, pricing, regulatory reporting data model • sExperience mapping data products to insurance business capabilitie s AI & Data Platform Experti • seExperience designing and implementin • g:AI/ML platforms (MLOps, model lifecycle management, feature store • s)AI-enabled data pipelines and intelligent automation framewor • ksExposure t • o:GenAI / LLM use cases in data (RAG, knowledge graphs, copilot • s)AI-driven migration and code/data modernisation approach es Technical Expert • iseStrong hands-on / architectural expertise • in:Azure data ecosystem (ADF, Synapse, Fabric, AD • LS)Databricks (Delta Lake, Spark, ML workflo • ws)Power BI (enterprise analytics & semantic lay • er)Strong grounding • in:Data modelling (dimensional, domain-driv • en)Data governance, lineage, catalogu • ingIntegration patterns (batch, streaming, AP Is)Leadership & Consulting Ski • llsExecutive stakeholder engagement (CIO/CDO/AI leade • rs)Ability to translate business problems into data + AI soluti • onsStrong storytelling and influencing capabil • ityExperience building data/AI CoEs and scalable delivery mod els