AI Architect

NTT DATA, Inc.; Dallas (TX); On-site; USD 120,000 - 150,000; Full time

Job summary

NTT DATA, Inc. is seeking an AI Architect in Dallas to design and lead enterprise-scale AI, ML, and Generative AI solutions on AWS and Azure. You will be responsible for end-to-end AI architecture, ensuring integration with enterprise systems and governance standards.

The ideal candidate will have a robust background in MLOps, AI frameworks, and cloud platforms. The position offers opportunities to lead innovative projects in a collaborative environment and contribute to AI-driven solutions.

Qualifications

• 7+ years deep knowledge of MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines.
• 5+ years advanced expertise deploying on AWS, Azure AI, or Google Vertex AI.
• 5+ years proficiency designing feature stores, vector databases, and real-time/batch data pipelines.
• 3-5 years familiarity with LLMs, Generative AI, RAG, PyTorch, or TensorFlow.

Responsibilities

• Define end-to-end blueprints for AI solution architecture.
• Embed AI/ML features into legacy applications.
• Implement ethical AI guardrails and data privacy protections.
• Establish CI/CD for AI and model retraining processes.
• Design architecture for autonomous decision-making systems.

Skills

MLOps

Containerization

CI/CD pipelines

AWS

Azure AI

Data Management

LLMs

Generative AI

RAG

PyTorch

TensorFlow

Job description

Job Title: AI Architect

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. Responsible for end-to-end AI solution architecture, ensuring alignment with enterprise systems, scalability, governance, and integration into the broader IT landscape. Requires deep expertise in Retrieval-Augmented Generation (RAG) and Agentic AI architecture on cloud-native platforms.

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, compliance).


• AI Reusable Utility – Develops reusable components (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 foundational AI infrastructure, frameworks, and observability capabilities (telemetry, monitoring, metrics) required for scalable, reliable, governed AI operations.

Core Responsibilities

• Architectural Design – Define end-to-end blueprints spanning data ingestion, model training, inference, and continuous monitoring; ensure models scale efficiently and align with enterprise systems and governance standards.


• Enterprise Integration – Seamlessly 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 prevent performance degradation.


• Tech Stack Strategy – Make 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.


• Architect solutions for batch and real‑time inference workloads on AWS and Azure.


• RAG (Retrieval‑Augmented Generation)
  
  
  
  • 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‑based AI systems.
  
  


• Agentic AI & Autonomous Systems
  
  
  
  • Design 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 use cases such as AI assistants, Copilot‑integrated workflows, task automation, and decision intelligence.
  
  


• MLOps & LLMOps
  
  
  
  • Define and implement 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.
  
  

Required Skills

• Engineering Foundation – 7+ years deep knowledge of MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines.


• Cloud Platforms – 5+ years advanced expertise deploying on AWS, Azure AI, or Google Vertex AI.


• Data Management – 5+ years proficiency designing feature stores, vector databases, and real‑time/batch data pipelines.


• AI/ML Frameworks – 3‑5 years familiarity with LLMs, Generative AI, RAG, PyTorch, or TensorFlow.

#LI-NorthAmerica

Equal Opportunity Employment

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 Pay Transparency information, please click here. For EEO rights under the law, please click here. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications.

Nearest Major Market: Dallas

Nearest Secondary Market: Fort Worth