AI Full stack Developer(.NET +React+ Gen AI)

Project Role: AI Architect & .NET full stack Developer

GEN AI +React +.NET

10+Years_Noida_Gurugram_Pune_Bangalore

Project Role Description

As an AI Architect & .NET Developer, you will be responsible for designing and governing end-to-end AI architectures on the Azure ecosystem that enable 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, and OCR to process complex unstructured insurance documents such as loss runs, policy forms, claims reports, and 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.

Must-Have Skills

1. GenAI Architecture
2. .NET (Backend) and React (Frontend) Development
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

Role Summary

As an AI Architect & .NET Developer, you will be responsible for designing and governing end-to-end AI architectures on the Azure ecosystem that enable 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, and OCR to process complex unstructured insurance documents such as loss runs, policy forms, claims reports, and 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

1. 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

2. 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.

3. 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 such as LangGraph, AutoGen, and CrewAI in production scenarios.

4. 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.

5. 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.

6. Evaluation, Quality & Optimization

• Establish evaluation frameworks for GenAI solutions, including:

• Precision, recall, and F1 metrics

• Grounding and hallucination detection

• Consistency and explainability checks

7. Collaboration & Leadership

• Partner with business stakeholders across Underwriting, Claims, Actuarial, and 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