Project Role Description
As an AI Architect & .NET developer, you will be responsible for designing and
governing end-to-end AI architectures on Azure ecosystem that enables intelligent
automation and decision support across insurance functions such as underwriting,
claims, reinsurance, and document-heavy operations.
The role focuses on building scalable, secure, and production-grade 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 enterprise-ready,
cost-efficient, 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
- Retrieval-Augmented 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 end-to-end AI architectures on Azure ecosystem that enables intelligent
automation and decision support across insurance functions such as underwriting,
claims, reinsurance, and document-heavy operations.
The role focuses on building scalable, secure, and production-grade 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 enterprise-ready,
cost-efficient, and aligned with regulatory and operational constraints.
Key Responsibilities
Architecture & Solution Design
- Act as an AI Architect and SME for GenAI-driven insurance use cases
- Define end-to-end AI architecture for unstructured document ingestion,
reasoning, and output generation
- Design LLM-centric 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 multi-step reasoning, task decomposition, and
tool orchestration
- Define patterns for human-in-the-loop, approvals, and exception handling
- Drive adoption of agent orchestration frameworks (LangGraph, AutoGen,
CrewAI) in production scenarios
RAG & Knowledge Architecture
- Design RAG-based 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
- API-first design
- Microservices-based architectures
- Experience integrating AI solutions into enterprise systems
Skills: ai,ocr,.net,azure,prompt,architecture
