As an AI Architect & .NET developer, you will be responsible for designing and governing endtoend AI architectures on Azure ecosystem that enables intelligent automation and decision support across insurance functions such as underwriting, claims, reinsurance, and documentheavy operations.
The role focuses on building scalable, secure, and productiongrade GenAI platforms leveraging LLMs, Agentic AI, OCR to process complex unstructured insurance documents (e.g., loss runs, policy forms, claims reports) and generate accurate, explainable, and auditable outputs.
You will define architectural patterns, lead the implementation team, and partner with business and technology stakeholders to ensure AI solutions are enterpriseready, costefficient, and aligned with regulatory and operational constraints.
Must Have Skills
- GenAI Architecture
- .NET (Backend) and React (Frontend) Developer
- Azure AI / Azure AI Foundry experience/ Vector Databases using Azure AI search
- Prompt Engineering & LLM Design
- RetrievalAugmented Generation (RAG) Architectures
Good to Have Skills
- Insurance Domain Knowledge (P&C / Commercial Lines / Reinsurance)
- Agentic AI Frameworks (LangGraph, AutoGen, CrewAI, etc.)
- OCR systems for document ingestion and classification
- AI Governance & Token Economics
Role Summary
As an AI Architect & .NET developer, you will be responsible for designing and governing endtoend AI architectures on Azure ecosystem that enables intelligent automation and decision support across insurance functions such as underwriting, claims, reinsurance, and documentheavy operations.
The role focuses on building scalable, secure, and productiongrade GenAI platforms leveraging LLMs, Agentic AI, OCR to process complex unstructured insurance documents (e.g., loss runs, policy forms, claims reports, bordereaux) and generate accurate, explainable, and auditable outputs.
You will define architectural patterns, lead the implementation team, and partner with business and technology stakeholders to ensure AI solutions are enterpriseready, costefficient, and aligned with regulatory and operational constraints.
Key Responsibilities Architecture & Solution Design
- Act as an AI Architect and SME for GenAIdriven insurance use cases
- Define endtoend AI architecture for unstructured document ingestion, reasoning, and output generation
- Design LLMcentric and hybrid AI architectures combining:
- OCR
- RAG systems
- Agentic workflows
GenAI & Prompt Architecture
- Design and govern prompt strategies and prompt frameworks for:
- Loss run and insurance document extraction & normalization
- Claims summarization, triage, and fraud signal generation
- Underwriting risk assessment and decision support
- Establish prompt versioning, testing, and optimization standards for enterprise use
Agentic AI & Workflow Orchestration
- Architect Agentic AI systems for multistep reasoning, task decomposition, and tool orchestration
- Define patterns for humanintheloop, approvals, and exception handling
- Drive adoption of agent orchestration frameworks (LangGraph, AutoGen, CrewAI) in production scenarios
RAG & Knowledge Architecture
- Design RAGbased knowledge architectures for policy, claims, and underwriting data
- Define chunking, embedding, retrieval, and grounding strategies
- Ensure traceability and explainability of generated outputs
Enterprise & Platform Architecture - Azure
- Drive architectural decisions related to:
- Scalability and performance
- Cost optimization of LLM usage
- Security, data privacy, and access control
- Auditability and regulatory compliance
- Define reference architectures and reusable components for multiple insurance use cases
Evaluation, Quality & Optimization
- Establish evaluation frameworks for GenAI solutions, including:
- Precision, recall, and F1 metrics
- Grounding and hallucination detection
- Consistency and explainability checks
Collaboration & Leadership
- Partner with business stakeholders (Underwriting, Claims, Actuarial, Legal) to shape AI roadmaps
- Technical project lead experience 7
- Guide and mentor .net developers, react developers, and GenAI developers
- Define best practices, standards, and architectural guardrails for GenAI adoption
Technical Stack & Platform Experience
- Programming & Frameworks
- Strong proficiency in .NET/React
- GenAI & LLM Platforms
- Azure OpenAI APIs / enterprise LLM platforms
- Architecture & Integration
- APIfirst design
- Microservicesbased architectures
- Experience integrating AI solutions into enterprise systems
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