Job Description

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

1. GenAI Architecture
2. NET (Backend) and React (Frontend) Developer
3. Azure AI / Azure AI Foundry experience/ Vector Databases using Azure AI search
4. Prompt Engineering & LLM Design
5. Retrieval‑Augmented Generation (RAG) Architectures

Good to Have Skills

1. Insurance Domain Knowledge (P&C / Commercial Lines / Reinsurance)
2. Agentic AI Frameworks (LangGraph, AutoGen, CrewAI, etc.)
3. OCR systems for document ingestion and classification
4. AI Governance & Token Economics

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