AI Architect

Talentify; Dallas (TX); On-site; USD 180,000 - 250,000; Full time

Job summary

NTT DATA in Dallas seeks an experienced AI Architect to design and lead enterprise-scale AI, ML, and Generative AI solutions on AWS and Azure, with Copilot as the UX layer. You will define end-to-end architecture, ensure scalability, governance, and integration with existing IT landscapes.

The role covers RAG, multi-agent AI, MLOps, and cloud-native platforms, plus leading collaborations with data scientists and software engineers to deliver production-ready AI systems.

Qualifications

• 7+ years in MLOps, CI/CD, and containerization.
• Experience deploying AI on AWS/Azure with enterprise scale.
• Proficiency with feature stores and vector databases.
• Familiarity with LLMs, Generative AI, and RAG concepts.

Responsibilities

• Design end-to-end AI architectures spanning data ingestion, model training, deployment and monitoring.
• Embed AI/ML features into enterprise apps, ERPs, and cloud-native systems.
• Establish governance, risk management, data privacy, and explainability standards.
• Develop and maintain MLOps pipelines, model versioning, monitoring and retraining, drift detection.
• Lead cross-functional teams and guide enterprise AI strategy across business lines.

Skills

MLOps

Docker/Kubernetes

CI/CD pipelines

Cloud platforms (AWS/Azure)

LLMs / Generative AI

RAG

Tools

PyTorch

TensorFlow

Vector databases (Pinecone/FAISS/Milvus)

Job description

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

1. 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.
2. Enterprise Integration: Seamlessly embed AI/ML features and multi-agent workflows into legacy applications, ERPs, and cloud-native systems.
3. Governance & Compliance: Implement ethical AI guardrails, model risk management, data privacy protections and explainability standards.
4. Scalability & MLOps: Establish CI/CD for AI, model versioning, automated retraining, and drift detection to prevent performance degradation.
5. Tech Stack Strategy: Make crucial "build vs. buy" decisions for infrastructure, weighing tradeoffs of on-premises, hybrid, and cloud environments.
6. Leadership & Collaboration:
7. Serve as a technical thought leader for AI, GenAI, and data platforms.
8. Mentor data scientists, ML engineers, and data engineers.
9. Collaborate with business and product teams to translate requirements into AI-driven solutions.
10. Evaluate emerging AI technologies and guide strategic adoption.
11. AI, ML & GenAI Architecture
    • Design and define end-to-end AI solution architectures covering data ingestion, model training, deployment, monitoring, and governance, ensuring alignment with enterprise systems and IT landscape while meeting scalability and governance standards.
12. Design scalable, cloud-native AI platforms on AWS and Azure.
13. Architect solutions for both batch and real-time inference workloads.
14. RAG (Retrieval-Augmented Generation)
    • 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.
15. 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 for agent-based systems.
    • Enable enterprise use cases such as AI assistants, Microsoft Copilot-integrated workflows, task automation, and decision intelligence.
16. MLOps & LLMOps
17. Define and implement MLOps / LLMOps frameworks for CI/CD, versioning, monitoring, and drift detection.
18. Enable experimentation, evaluation, and governance of ML models and LLM-based systems.
19. Ensure compliance with security, privacy, and responsible AI guidelines.
20. Cloud & Platform Engineering
21. Architect AI solutions on AWS and Azure as the primary cloud platforms, integrating Microsoft Copilot as the enterprise user experience layer.
22. Integrate AI platforms with enterprise applications, APIs, and data sources.
23. Design highly available, secure, and scalable AI systems.

Required Skills

• Engineering Foundation: 7+ years of deep knowledge of MLOps, containerization (Docker/Kubernetes), and CI/CD pipelines.
• Cloud Platforms: 5+ years of advanced expertise in deploying on major hyperscalers like AWS Machine Learning, Azure AI, or Google Vertex AI.
• Data Management: 5+ years of Proficiency in designing feature stores, vector databases, and real-time/batch data pipelines.
• AI/ML Frameworks: 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.

#LI-NorthAmerica

About NTT DATA

NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.

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