Senior Data Architect (GenAI)

A leading enterprise insurance firm within a highly regulated industry is seeking an experienced Senior Data Architect (GenAI) with deep expertise in data architecture, cloud platforms, and AI/ML technologies to drive next-generation data and AI transformation initiatives.

This role sits at the forefront of innovation, leading the design and implementation of scalable, secure, and high-performance data ecosystems that power advanced analytics and Generative AI use cases. You will play a critical role in translating business needs into enterprise-grade AI data platforms, supporting use cases such as customer engagement, risk assessment, claims optimization, and intelligent automation.

Key responsibilities

• Define and drive enterprise-wide AI data architecture strategies, standards, and best practices to support AI and GenAI initiatives
• Design and implement end-to-end data architectures for AI/ML and GenAI use cases, including data ingestion, processing, storage, and model integration
• Develop and maintain architecture roadmaps and blueprints for AI platforms, ensuring alignment with overall IT and business strategies
• Lead the design of scalable and high-performance data pipelines, ensuring data quality, reliability, and SLA adherence through monitoring and governance frameworks
• Provide technical leadership in AI data platform design, including support for LLM integration, retrieval-augmented generation (RAG), and agentic AI workflows
• Support domain-specific use cases such as policy analysis, claims processing, fraud detection, underwriting insights, and customer service automation
• Troubleshoot complex issues across data pipelines, platforms, and AI model deployments, driving innovative and sustainable solutions
• Collaborate with enterprise architecture, engineering, and business teams to translate requirements into robust and future-ready solutions
• Participate in architecture governance, design reviews, and solution assessments, ensuring compliance with enterprise standards and regulatory expectations
• Establish and enforce data governance, security, and compliance frameworks, ensuring adherence to privacy regulations and responsible AI practices
• Drive continuous improvement, innovation, and adoption of modern data and AI technologies, contributing to reusable patterns and best practices

Candidate profile

• Degree in Computer Science, Information Technology, Engineering, or a related discipline
• Minimum 10 years of experience in data architecture or data engineering, within complex enterprise environments
• At least 4 years of hands-on experience in AI/ML data architecture design and implementation at scale
• Strong expertise in cloud platforms (Azure preferred) and modern data ecosystem design
• Proficiency in data engineering and analytical tools (e.g., Python, R, machine learning frameworks)
• Hands-on experience with Generative AI technologies, including:
• LLM integration and prompt engineering
• Retrieval-augmented generation (RAG)
• AI model governance and lifecycle management
• Emerging paradigms such as agentic AI frameworks
• Experience operating within regulated environments (e.g., insurance, financial services, or healthcare), with strong awareness of data privacy, governance, and compliance requirements
• Strong experience in defining architecture strategies, roadmaps, and standards, with the ability to influence stakeholders
• Proven ability to work in complex, fast-paced environments, managing multiple priorities and delivering high-impact solutions
• Excellent communication and stakeholder management skills, with experience collaborating across business and technical teams

About the organization

A large-scale, forward-looking enterprise undergoing significant data and AI transformation, focused on leveraging advanced analytics and emerging AI technologies to drive business innovation, enhance customer experiences, and optimize risk and operational outcomes.

The organization operates in a regulated industry environment, placing strong emphasis on governance, security, and ethical AI adoption, while fostering a culture of technology innovation, collaboration, and continuous improvement.

Keywords

AI Data Architect, Data Architecture, Generative AI, LLM, RAG, Agentic AI, Insurance Analytics, Claims Automation, Fraud Detection, Data Platform, Cloud Architecture, Azure, Machine Learning, Digital Transformation