AI Architect
Posted 7 Oct 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 10+ yrs
- Management level
- People management
No salary listed for this role.
Req ID: 375661
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a AI Architect to join our team in Dallas, Texas (US-TX), United States (US).
Job Title: AI Architect
Experience level: 10 + years
Job Summary
We are seeking an experienced AI Architect to design and lead enterprise-scale AI, ML, and Generative AI solutions built on AWS and Azure as the core AI foundation, with Microsoft Copilot as the primary user experience layer. The role is responsible for designing the end-to-end AI solution architecture, ensuring alignment with enterprise systems, scalability, and governance standards while integrating AI into the broader IT landscape. It requires deep expertise in RAG (Retrieval-Augmented Generation) and Agentic AI architecture on cloud-native platforms, enabling intelligent, scalable, and production-ready AI systems after understanding the current product architecture. The candidate should also be able to conduct POCs to demonstrate proof of design considerations.
Platform & Enablement Roles
- AI Platform Admin (M365, copilot Studio) Manages AI platforms and environments, including access provisioning, governance controls, and policy enforcement (e.g., DLP, security, and compliance).
- AI Reusable Utility Develops reusable components (e.g., prompts, connectors, APIs, templates) to accelerate AI solution delivery and promote standardization across use cases.
- AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) Designs and maintains the foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, and governed AI operations.
Core Responsibilities
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Architectural Design: Define the end-to-end blueprints spanning data ingestion, model training, inference, and continuous monitoring. design end-to-end artificial intelligence solutions ensuring models scale efficiently align with enterprise systems and meet governance standards. They act as the vital bridge linking theoretical AI models built by data scientists with production-ready, secure applications integrated into the broader IT landscape.
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Enterprise Integration: Seamlessly embed AI/ML features and multi-agent workflows into legacy applications, ERPs, and cloud-native systems.