AI Architect

Req ID: 375661 Job Title: AI Architect Location: Dallas, Texas, United States 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 with Microsoft Copilot as the primary user experience layer. The role is responsible for designing end‑to‑end AI solution architecture, ensuring alignment with enterprise systems, scalability, and governance, while integrating AI into the broader IT landscape. The candidate must have deep expertise in Retrieval‑Augmented Generation (RAG) and agentic AI architecture on cloud‑native platforms, enabling intelligent, scalable, and production‑ready AI systems, and conduct POCs demonstrating 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. AI Reusable Utility – Develops reusable components (prompts, connectors, APIs, templates) to accelerate AI solution delivery. AI Common Infrastructure, Framework & Observability Architect (AWS and Azure) – Designs and maintains foundational AI infrastructure, frameworks, and observability capabilities. Core Responsibilities Architectural Design – Define end‑to‑end blueprints for data ingestion, model training, inference, and continuous monitoring. Bridge theoretical AI models with production‑ready, secure applications. Enterprise Integration – Embed AI/ML features and multi‑agent workflows into legacy applications, ERPs, and cloud‑native systems. Governance & Compliance – Implement ethical AI guardrails, model risk management, data privacy protections, and explainability standards. Scalability & MLOps – Establish CI/CD for AI, model versioning, automated retraining, and drift detection to maintain performance. Tech Stack Strategy – Make build‑vs‑buy decisions for infrastructure, weighing on‑premises, hybrid, and cloud trade‑offs. Leadership & Collaboration 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. AI, ML & GenAI Architecture Design end‑to‑end AI solution architectures covering data ingestion, model training, deployment, monitoring, and governance. Build scalable, cloud‑native AI platforms on AWS and Azure. Architect solutions for both batch and real‑time inference workloads. Retrieval‑Augmented Generation (RAG) Architect and implement RAG pipelines using structured and unstructured enterprise data. Design ingestion, chunking, embedding, and retrieval strategies. Integrate vector databases (Pinecone, FAISS, Milvus, Azure AI Search, Amazon OpenSearch). Ensure relevance, freshness, observability, and security of RAG systems. Agentic AI & Autonomous 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. Enable enterprise use cases such as AI assistants, Copilot‑integrated workflows, task automation, and decision intelligence. MLOps & LLMOps 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. Cloud & Platform Engineering Architect AI solutions on AWS and Azure, integrating Microsoft Copilot as the enterprise UX layer. Integrate AI platforms with enterprise applications, APIs, and data sources. Design highly available, secure, and scalable AI systems. Required Skills 7+ years of experience in MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines. 5+ years deploying on major hyperscalers such as 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. 3–5 years of familiarity with Large Language Models, Generative AI, RAG, and frameworks like PyTorch or TensorFlow. EEO Statement NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. For information on your EEO rights under the law, please see the EEO Policy Statement and Pay Transparency information. #J-18808-Ljbffr NTT DATA North America