Azure AI Architect

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 Show more Show less