AI Security Architect

NTT DATA North America; Boston (MA); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Hybrid work

Job summary

NTT DATA North America currently seeks an AI Security Architect to join our client team in Boston, MA (Hybrid – Work from Client Office). The role focuses on defining the enterprise AI strategy, designing scalable AI/ML/Generative AI architectures, and leading secure, compliant AI implementations across cloud and on‑prem environments.

The position requires bridging business goals with cutting-edge AI technologies, establishing governance, and mentoring AI engineers and data scientists to deliver

Qualifications

• Define enterprise AI architecture aligned with business strategy.
• Design scalable AI, ML, and Generative AI solution architectures.
• Establish enterprise AI governance, risk, and compliance.

Responsibilities

• Define enterprise AI architecture aligned with business strategy and digital transformation objectives.
• Design end-to-end AI solutions integrating enterprise applications, cloud platforms, data platforms, and AI models.
• Lead architecture reviews and technology assessments for AI initiatives.

Skills

AI security

Zero Trust

Cloud security

LLMOps

Security architecture

Tools

Azure AI

AWS

Vertex AI

OpenAI

Job description

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us.

NTT DATA's Client is currently seeking an AI Security Architect to join their team in Boston, Massachusetts (US-MA), United States (US).

Job Description

AI Security Architect
Level – L4
Location – US, Massachusetts (Boston Area) (Hybrid – Work from Client Office)
Position Start Date: 16 Aug 2026
Duration: 1 Year

Position Summary

• The AI Architect is responsible for defining the enterprise AI strategy, designing scalable AI and Generative AI solutions, and leading the technical architecture for AI-driven products and business transformation initiatives.
• The role bridges business objectives with emerging AI technologies, ensuring secure, scalable, ethical, and compliant AI implementations across cloud and on-premises environments.

The AI Architect works closely with business stakeholders, enterprise architects, data engineers, security teams, application developers, and data scientists to deliver production-ready AI solutions while establishing enterprise AI governance, standards, and best practices.

Key Responsibilities

• AI Strategy & Architecture
• Define enterprise AI architecture aligned with business strategy and digital transformation objectives.
• Design scalable AI, Machine Learning (ML), and Generative AI solution architectures.
• Develop AI reference architectures, reusable frameworks, and implementation standards.
• Evaluate emerging AI technologies and recommend adoption strategies.
• Establish enterprise AI roadmaps and technology blueprints.
• Solution Design
• Design end-to-end AI solutions integrating enterprise applications, cloud platforms, APIs, and data platforms.
• Architect Retrieval-Augmented Generation (RAG), AI agents, copilots, intelligent automation, and conversational AI solutions.
• Define model selection strategies for LLMs, foundation models, and traditional ML models.
• Design vector databases, prompt engineering frameworks, embeddings, and orchestration pipelines.

AI Platform & Engineering

• Design AI platforms leveraging Azure AI, AWS AI, Google Vertex AI, OpenAI, Anthropic, or similar technologies.
• Define scalable MLOps and LLMOps architectures.
• Establish model lifecycle management, CI/CD pipelines, monitoring, and version control.
• Optimize AI infrastructure for performance, scalability, reliability, and cost efficiency.
• Governance, Risk & Compliance
• Establish AI governance frameworks, responsible AI principles, and model risk management practices.
• Ensure compliance with AI regulations, privacy requirements, and security standards.
• Define controls for data protection, explainability, bias detection, model monitoring, and auditability.
• Collaborate with GRC, Privacy, and Security teams to implement AI risk controls.

Security Architecture

• Design secure AI solutions following Zero Trust principles.
• Define security controls for AI models, APIs, prompts, embeddings, and training data.
• Implement identity management, encryption, access controls, and secure deployment practices.
• Address AI-specific threats including prompt injection, model poisoning, data leakage, and adversarial attacks.

Technical Leadership

• Provide architectural guidance to AI engineers, data scientists, and development teams.
• Lead architecture reviews and technology assessments.
• Mentor technical teams on AI best practices and emerging technologies.
• Drive innovation through proof-of-concepts (POCs), pilots, and accelerator development.

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