AI Architect-Insurance

Job Description Insurance AI Architect Insurance Industry & Platform Expertise Duck Creek Technologies ? Working knowledge of Duck Creek OnDemand, Duck Creek Policy, Claims, Billing, and Rating modules; understanding of Duck Creek's Author/Manuscript configuration model, Duck Creek Data Insights, and Duck Creek's low-code/no-code extensibility framework Optional - Guidewire InsuranceSuite ? Basic knowledge of PolicyCenter, ClaimCenter, and BillingCenter architecture, including Gosu scripting, PC/CC/BC data model, Integration Gateway, Cloud API, and Guidewire Cloud Platform (GWCP); familiarity with UI framework and Guidewire Marketplace apps Platform integration patterns ? Experience with how AI services plug into both platforms via REST/SOAP APIs, event-driven architectures (Guidewire Cloud Events, Duck Creek's integration points), webhooks, and middleware (MuleSoft, Boomi, Azure Integration Services) Understanding of P&C Commercial & Specialty product structures ? rating algorithms, forms/endorsements, exposure bases, and how these map into platform data models AI/ML Technical Stack LLM & GenAI frameworks ? LangChain, LlamaIndex, Semantic Kernel; prompt engineering, RAG architecture design, and agentic frameworks ( CrewAI, or similar) for multi-step underwriting/claims workflows Foundation model platforms ? Practical experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, and Anthropic Claude/API; model selection criteria (cost, latency, accuracy) for insurance-specific tasks Traditional ML/predictive modeling ? Regression, gradient boosting (XGBoost/LightGBM) for risk scoring and pricing models; familiarity with actuarial modeling concepts as they intersect with ML Document AI/IDP ? OCR and intelligent document processing (Azure Form Recognizer, AWS Textract) for submission intake, ACORD forms, loss runs, and SOV (statement of values) processing ? a common Commercial/Specialty use case MLOps & deployment ? Model versioning, monitoring, and governance tooling (MLflow, Azure ML, SageMaker); CI/CD for AI pipelines integrated with platform release cycles Microsoft AI Platform Expertise ? Copilot Studio, Foundry and Microsoft ecosystem integration Platform decision-making ? Judgment on when to use Copilot Studio (low-code, business-user-facing agents) vs. AI Foundry (custom, code-first, higher-control agentic solutions) vs. open-source/other cloud AI stacks, based on client maturity, governance needs, and use case complexity Data & Cloud Architecture Cloud platforms ? Azure, AWS, or GCP; landing zone design, data lake/lakehouse architecture for insurance data Data engineering ? ETL/ELT pipelines pulling from Duck Creek/Guidewire data models into analytics or AI-ready stores; understanding of insurance data standards (ACORD, ISO) API & microservices design ? RESTful API design, event streaming (Kafka), and how to expose AI capabilities as composable services consumable by both platforms Governance & Compliance Working knowledge of model risk management , explainability techniques Familiarity with SOC 2, data privacy (state insurance data regulations), and responsible AI frameworks as applied to carrier environments Executive & Stakeholder Communication CXO-level engagement Executive storytelling & influence Trusted advisor positioning Change management fluency