AI Security Architect

AI Security Architect

NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. NTT DATA's client is currently seeking an AI Security Architect to join their team in Boston, Massachusetts (US-MA), United States (US).

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.
• Stakeholder Management
• Collaborate with Enterprise Architects, CISO, business leaders to identify AI opportunities.
• Translate business requirements into AI solution architectures.
• Present architecture designs and technical recommendations to executive stakeholders.
• Support pre-sales activities, solution proposals, and client workshops.

Required Qualifications

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• Master's degree preferred.
• 10+ years of experience in software engineering, cloud architecture, or enterprise solution architecture.
• 5+ years of experience designing enterprise AI or Machine Learning solutions.

Required Technical Skills

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Generative AI
• Large Language Models (LLMs)
• Natural Language Processing (NLP)
• Computer Vision
• Reinforcement Learning (preferred)
• GenAI Technologies
• OpenAI
• Azure OpenAI
• Anthropic Claude
• Google Gemini
• Meta Llama
• Mistral
• Hugging Face
• AI Frameworks
• LangChain
• LangGraph
• LlamaIndex
• Semantic Kernel
• CrewAI
• AutoGen
• Programming
• Python
• Java
• C#
• REST APIs
• SQL
• JavaScript (preferred)
• Cloud Platforms
• Microsoft Azure AI
• AWS AI Services
• Google Cloud Vertex AI
• Data Technologies
• Azure Data Platform
• Databricks
• Snowflake
• Vector Databases (Pinecone, Weaviate, Milvus, Azure AI Search)
• SQL/NoSQL databases
• MLOps / LLMOps
• MLflow
• Azure ML
• Kubeflow
• Docker
• Kubernetes
• GitHub Actions
• Azure DevOps
• Security & Governance
• AI Governance Frameworks
• Responsible AI
• Model Monitoring
• Data Privacy
• AI Risk Management
• AI Security
• India DPDP Act
• Soft Skills
• Strategic thinking and innovation
• Strong analytical and problem-solving abilities
• Executive-level communication and presentation skills
• Leadership and mentoring capabilities
• Stakeholder management
• Cross-functional collaboration
• Ability to simplify complex technical concepts for business audiences
• Preferred Certifications
• Microsoft Certified: Azure AI Engineer Associate
• Microsoft Certified: Azure Solutions Architect Expert
• AWS Certified Machine Learning Engineer
• Google Professional Machine Learning Engineer
• Databricks Certified Machine Learning Professional
• NVIDIA AI Certifications
• TOGAF
• Certified Information Systems Security Professional (CISSP) (preferred)
• ISO/IEC 42001 Lead Implementer or Lead Auditor (preferred)
• Key Deliverables
• Enterprise AI Strategy and Roadmap
• AI Reference Architecture
• AI Solution Designs
• GenAI and Agentic AI Architecture
• AI Governance Framework
• AI Security Architecture
• MLOps/LLMOps Framework
• Architecture Review Reports
• Technology Evaluation and Recommendation Documents
• AI Standards and Best Practices
• Proof of Concepts (POCs) and Technical Accelerators

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