Sr. Data and AI Architect (Hybrid)

About the role

In this role, you will be responsible for designing and implementing enterprise-wide data and AI solutions that enable AI-led transformation across the organization. This role bridges traditional data architecture with modern AI engineering — driving adoption of large language models (LLMs), generative AI, agentic AI, multi-agent orchestration, Model Context Protocol (MCP), retrieval-augmented generation (RAG), and AI-integrated front-end chatbots — while identifying and delivering high-impact use cases that accelerate enterprise AI adoption.

Responsibilities

• Design and oversee enterprise-critical data and AI architecture solutions, ensuring scalability, security, data integrity, and optimal performance across cloud and on-premises platforms.
• Lead the strategy and architecture for AI platforms, including LLM integrations, agentic AI, multi-agent orchestration frameworks, Model Context Protocol (MCP) implementations, retrieval-augmented generation (RAG) pipelines, vector databases, and other generative AI solutions.
• Architect and oversee agentic AI, including autonomous agent design, tool-use patterns, agent memory management, and human-in-the-loop controls for enterprise-grade reliability and governance.
• Design and implement MCP-based integrations to enable structured, context-aware communication between AI agents and enterprise data sources, APIs, and services.
• Architect end-to-end AI-enabled systems, collaborating with data engineers and front-end developers to deliver production-ready intelligent systems.
• Design and maintain enterprise data models (conceptual, logical, and physical) that serve as the foundation for AI-powered applications, database design, and data integration efforts.
• Provide thought leadership on generative AI, agentic AI, and emerging AI protocol trends (e.g., MCP, A2A) and their applicability to enterprise business problems, particularly within the life insurance domain.
• Evaluate and implement cloud-native AI services (AWS Bedrock, Amazon Q, or equivalent) and establish best practices for responsible AI, AI governance, and explainability.
• Communicate complex AI and data architecture concepts to diverse stakeholders at all organizational levels, including executive leadership.
• Evaluate and resolve complex data architecture challenges requiring analysis of data quality issues, SQL optimization, ETL pipeline design, and conceptual/logical/physical data modeling to understand enterprise-wide implications.
• Influence and establish data architecture best practices across the organization, focusing on cloud data services, modern data stack technologies, and scalable data integration patterns.
• Architect and oversee ETL/ELT workflows, data pipelines, and integration processes to ensure efficient data movement and transformation across cloud and on-premises data platforms.
• Contribute to and oversee front-end solution design using .NET (C#, ASP.NET Core, Web API) and Angular, ensuring seamless integration between AI/data back-end services and user-facing AI Chatbot.
• Partner with portfolio leaders to understand analytical and AI requirements into scalable technical solutions and actionable insights.
• Establish and enforce data and AI architecture standards, governance frameworks, and best practices, including agentic AI safety guardrails, across the organization.
• Mentor and guide data engineers, AI engineers, and front-end developers, providing technical leadership and knowledge transfer.
• Direct data and AI architecture activities to ensure successful delivery of critical enterprise initiatives and intelligent business solutions.

Requirements

• Bachelor's degree along with 10+ years of work experience in Data and 3+ years experience in AI Architecture & engineering.
• AWS Certified Solutions Architect or AWS Certified Data Analytics.
• AWS Certified AI Practitioner or equivalent AI certification (a plus).
• 10+ years of progressive experience in data architecture, data modeling, and enterprise data management.
• 3+ years of progressive experience in AI architecture and engineering.
• Demonstrated expertise in designing and implementing AI solutions at enterprise scale, including LLM, generative AI, and agentic AI integrations.
• Hands-on experience designing agentic AI systems, including multi-agent orchestration, tool-use patterns, skills framework, agent memory, planning loops, and human-in-the-loop controls.
• Hands-on experience designing and implementing MCP-based integrations to enable structured, context-aware communication between AI agents and enterprise data sources, tools, and APIs.
• Hands-on experience designing prompts, building prompt chains, and integrating LLMs (e.g., AWS Bedrock, Azure OpenAI) into enterprise agentic and generative AI workflows.
• Demonstrated proficiency in designing retrieval-augmented generation (RAG) pipelines, vector databases (e.g., Pinecone, OpenSearch), and knowledge grounding strategies for enterprise AI applications.
• Demonstrated understanding of AI explainability, agentic AI safety guardrails, data privacy, prompt governance, and ethical AI frameworks.
• Hands-on experience monitoring, tracing, and evaluating AI agent behavior and LLM outputs in production using observability platforms.
• Deep knowledge of AWS cloud services including Bedrock, Amazon Q, Glue, Redshift, S3, and cloud-native AI tooling.
• Hands-on experience with front-end development using .NET (C#, ASP.NET Core, Web API) and Angular for building AI-integrated enterprise applications.
• Deep understanding of data architecture patterns, data warehousing, dimensional modeling, data lakes, and modern data stack technologies.
• Extensive experience with SQL development, query optimization, performance tuning, and database design across multiple platforms.
• Hands-on experience with ETL/ELT tools such as Informatica, SSIS, AWS Glue, or similar data integration platforms.
• Proven track record of leading data and AI architecture initiatives and delivering complex enterprise projects.
• Demonstrated ability to mentor and guide cross-functional teams while providing thought leadership on data and AI initiatives.
• Demonstrated independent judgment and critical thinking to evaluate complex challenges and recommend optimal solutions.
• Demonstrated strategic thinking to design scalable, enterprise-level data and AI solutions that meet current and future business needs.
• Experience working with cross-functional teams and stakeholders at various organizational levels, with strong communication skills to articulate complex technical concepts to both technical and non-technical audiences.
• Knowledge of policy administration, actuarial concepts, claims processing, underwriting, and insurance-specific regulatory requirements; life insurance industry experience highly desirable.

Schedule

Hybrid position located in McKinney, TX. Work from home Monday & Friday, in office Tuesday–Thursday.