AI Architect Job at NTT DATA (Dallas)

Job Description

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.

Job Responsibility

Define the end-to-end blueprints spanning data ingestion, model training, inference, and continuous monitoring
Seamlessly embed AI/ML features and multi-agent workflows into legacy applications, ERPs, and cloud-native systems
Implement ethical AI guardrails, model risk management, data privacy protections and explainability standards
Establish CI/CD for AI, model versioning, automated retraining, and drift detection to prevent performance degradation
Make crucial 'build vs. buy' decisions for infrastructure, weighing tradeoffs of on-premises, hybrid, and cloud environments
Serve as a technical thought leader for AI, GenAI, and data platforms
Mentor data scientists, ML engineers, and data engineers
Collaborate with business and product teams to translate requirements into AI-driven solutions
Evaluate emerging AI technologies and guide strategic adoption
Design and define end-to-end AI solution architectures covering data ingestion, model training, deployment, monitoring, and governance
Design scalable, cloud-native AI platforms on AWS and Azure
Architect solutions for both batch and real-time inference workloads
Architect and implement RAG pipelines using structured and unstructured enterprise data
Design ingestion, chunking, embedding, and retrieval strategies for RAG systems
Integrate vector databases (e.g., Pinecone, FAISS, Milvus, Azure AI Search, Amazon OpenSearch)
Ensure relevance, freshness, observability, and security of RAG-based AI systems
Design Agentic AI architecture enabling autonomous decision-making and task execution
Orchestrate multi-agent systems using tools, memory, and reasoning workflows
Implement guardrails, human-in-the-loop controls, and observability for agent-based systems
Enable enterprise use cases such as AI assistants, Microsoft Copilot-integrated workflows, task automation, and decision intelligence
Define and implement MLOps / LLMOps frameworks for CI/CD, versioning, monitoring, and drift detection
Enable experimentation, evaluation, and governance of ML models and LLM-based systems
Ensure compliance with security, privacy, and responsible AI guidelines
Architect AI solutions on AWS and Azure as the primary cloud platforms, integrating Microsoft Copilot as the enterprise user experience layer
Integrate AI platforms with enterprise applications, APIs, and data sources
Design highly available, secure, and scalable AI systems

Requirements

7+ years of deep knowledge of MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines
5+ years of advanced expertise in deploying on major hyperscalers like AWS Machine Learning, Azure AI, or Google Vertex AI
5+ years of Proficiency in designing feature stores, vector databases, and real-time/batch data pipelines
3 to 5 years of familiarity with concepts like Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), and frameworks like PyTorch or TensorFlow
10+ years experience level

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