Principal Data and AI Architect

MSIG USA continues to grow!

Company Overview MSIG USA is the US-based subsidiary of MS&AD Insurance Group Holdings, Inc., one of the world’s top P&C carriers and a global Class 15 insurer, with A+ ratings and a reach that spans 40+ countries and regions. Leveraging our 350-year heritage, MSIG USA brings the financial strength, expertise, and global footprint to offer commercial insurance solutions that address your business’s unique risks.

Position Overview We are seeking a seasoned Enterprise Data & AI Architect at the Lead/Principal level to serve as the technical authority and strategic design leader for MSIG USA’s enterprise data platform and AI ecosystem. This is one of the most impactful and senior individual contributor roles within our Data, Analytics & AI organization.

You will be responsible for defining the overarching architecture across our data platforms, AI/ML systems, data governance frameworks, and integration patterns — ensuring they are cohesive, scalable, secure, and aligned with our P&C insurance business strategy. You will set the technical direction, establish engineering standards, guide platform decisions, and serve as a trusted advisor to the CDAO, technology leadership, and business domain leaders across Underwriting, Claims, Actuarial, Finance, and Reinsurance.

This is a role for a deeply technical, intellectually curious architect who is equally comfortable shaping long-term strategies and rolling up their sleeves to validate designs, prototype solutions, and guide engineering teams through complex delivery challenges.

Key Responsibilities Enterprise Architecture Strategy & Vision

• Define and own the enterprise data and AI architecture blueprint for MSIG USA, covering data ingestion, storage, transformation, serving, governance, and AI/ML deployment.
• Establish the target state architecture across cloud platforms, data products, AI systems, and integration patterns — with a clear, pragmatic roadmap from current to future state.
• Lead architecture governance — chairing design reviews, evaluating technology proposals, and enforcing standards across data engineering, AI/ML, and analytics teams.
• Translate MSIG USA’s business strategy and regulatory obligations into concrete, actionable architectural decisions with well-documented tradeoffs.
• Serve as the primary technical liaison between the CDAO, IT leadership, and enterprise architecture functions across MS&AD Insurance Group.

Data Platform Architecture

• Architect and evolve MSIG USA’s hybrid data platform built on Microsoft Fabric, Databricks, and Azure Data Lake (OneLake) — ensuring the platform supports batch, streaming, and real-time data workloads across all insurance domains.
• Design the Medallion (Bronze/Silver/Gold) architecture and enforce lakehouse best practices including Delta Lake, Unity Catalog, and data product publishing patterns aligned to Data Mesh principles.
• Define data modeling standards across entity types — Policy, Customer/Party, Claims, Exposure, Premium, Loss, and Reinsurance — ensuring consistency across the enterprise.
• Oversee the architecture of the Master Data Management (MDM) platform (Profisee) and its integration with upstream policy systems, downstream analytics, and the data lakehouse.
• Design enterprise data integration patterns — API-first, event-driven, and ETL/ELT architectures — connecting policy administration systems, claims platforms, financial systems, and external data providers to the central data platform.
• Ensure platform architecture meets high availability, disaster recovery, scalability, and cost efficiency requirements for a regulated insurance environment.

AI & Machine Learning Architecture

• Define the enterprise AI/ML architecture — spanning model development, training, deployment, monitoring, and governance — built on Databricks Mosaic AI, Azure Machine Learning, and Azure OpenAI.
• Architect the MLOps platform including CI/CD pipelines for ML models, feature store design, model registry, experiment tracking (MLflow), and production model serving.
• Design Generative AI and LLM architectures — including Retrieval-Augmented Generation (RAG) systems, agentic frameworks, prompt management, and responsible AI guardrails — for insurance use cases across underwriting, claims, and actuarial functions.
• Establish AI governance and model risk management frameworks ensuring all production AI systems are explaina…