AI Architect

Req ID: 386628

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.

We are currently seeking a AI Architect to join our team in Plano, Texas (US-TX), United States (US).

Role Summary

As an AI Architect at Lead Advisor level, you will lead large-scale AI programs spanning multiple client engagements, shape the AI vision and roadmap for the client organizations you serve and for our AI practice, and set the architectural standards that other architects apply. You will be the senior technical authority on the programs you lead and a trusted advisor to client executive leadership.

The role spans the AI lifecycle — from discovery and solution design through proof of concept, production implementation, and operational improvement. The scope includes machine learning, predictive analytics, natural language processing, computer vision, intelligent automation, and Generative AI.

This is a hands-on architecture role. Expect roughly half your time on architecture, client technical leadership, design governance, and mentoring, and roughly half hands-on — reference implementations, prototypes, technical spikes, integration work, and solution and code reviews. Engagement models vary, and some client contexts require sustained hands-on delivery alongside the engineering team.

Key Responsibilities

• Lead large-scale AI initiatives and programs spanning multiple client engagements, from conception through production and continuous improvement.
• Shape the AI vision and multi-year roadmap for the client organizations you serve, and develop long-term strategic plans for their AI initiatives.
• Set and govern architectural standards, patterns, and technical direction across the engagements in your portfolio.
• Serve as the senior technical escalation point for complex, contested, or high-risk architectural decisions, driving them to resolution across client and internal stakeholders.
• Define solution architecture across applications, integration, data, AI/ML, infrastructure, security, networking, identity, observability, and operations.
• Design AI solution patterns across ML and GenAI — LLM applications, retrieval-augmented generation, agents, orchestration, embeddings, vector and graph-based retrieval, model integrations, and evaluation — and build or guide the reference implementations that prove them.
• Select fit-for-purpose AI techniques, model types, platforms, data patterns, and deployment approaches based on business value, data readiness, quality, performance, cost, security, and compliance needs.
• Optimize AI models and inference workloads for performance, scalability, and cost, and innovate AI infrastructure patterns — including distributed computing, accelerator use, and data storage for AI — for performance and future scale.
• Architect public-cloud, private-cloud, hybrid, and on-premises deployment models as client requirements warrant.
• Apply secure-by-design practices — authentication, RBAC, secrets management, encryption, private networking, data protection, auditability, and authorization-aware data retrieval.
• Own the responsible-AI and governance posture for your programs, including content safety, PII protection, bias mitigation, prompt-injection defenses, human review, model risk management, and compliance with data privacy regulation across the jurisdictions your clients operate in.
• Establish MLOps, LLMOps, and GenAIOps practices for CI/CD, model and prompt versioning, infrastructure as code, testing, evaluation, monitoring, tracing, incident response, and lifecycle management.
• Partner with client business, product, data, engineering, infrastructure, and security stakeholders to drive technical decisions, resolve risks, and communicate trade-offs.
• Communicate AI vision, architecture, and trade-offs to client executive leadership and enterprise architecture functions.
• Mentor and develop senior AI architects and advisors, lead architecture review forums, and raise the technical bar across the practice.
• Own the reference architecture, accelerator, and delivery playbook portfolio for the AI practice; stay ahead of emerging AI technologies; and shape selected strategic pursuits through discovery workshops, solution shaping, estimates, and proposal input.

Basic Qualifications

The experience periods below overlap and are not additive.

• Bachelor’s degree in computer science, engineering, data science, information systems, or a related technical discipline.
• 12+ years in software engineering, cloud, data engineering, AI/ML, architecture, or enterprise technology delivery.
• 6+ years designing and delivering production AI or ML solutions.
• 2+ years delivering production Generative AI or LLM-based systems, including LLM APIs, RAG, embeddings, vector and graph-based retrieval, evaluation, guardrails, and agentic workflows.
• 8+ years in software engineering with professional-level proficiency in at least one mainstream language — Python, Java, C#, C++, TypeScript/JavaScript, Go, or equivalent — including designing and integrating APIs and backend services using REST, gRPC, event-driven, or asynchronous patterns.
• 5+ years leading technical design and implementation in complex enterprise environments, with accountability for architectural outcomes.
• 3+ years architecting AI solutions across more than one deployment model (public cloud plus private cloud, hybrid, or on-premises) and establishing operational practice for AI systems — model and prompt versioning, artifact and experiment tracking, automated evaluation, monitoring and tracing, incident response, rollback — using CI/CD, infrastructure as code, and DevOps tooling.

Preferred Qualifications

• Master’s degree in AI/ML, computer science, engineering, data science, or a related field.
• 5+ years in consulting or professional services delivering technical solutions to external clients, including discovery workshops, solution shaping, effort estimation, and proposal input.
• 3+ years architecting secure enterprise systems — identity and authentication, authorization and RBAC, secrets management, encryption, network isolation, audit logging — and implementing responsible-AI controls in production, including content safety, PII protection, prompt-injection defenses, human review, and model risk management.
• Experience spanning Generative AI and one or more additional AI domains, such as classical ML, predictive analytics, NLP, computer vision, or intelligent automation.
• Experience across two or more major cloud AI platforms, including their managed model, retrieval, and orchestration services.
• Experience with container orchestration and GPU or accelerator infrastructure.
• Experience with model-serving and inference-optimization stacks, open-source or vendor, for self-hosted and private deployments.
• Experience with modern deep learning and classical ML frameworks, and the wider open-source model ecosystem.
• Experience with knowledge graphs, ontologies, semantic modelling, and graph databases, including graph-based or hybrid retrieval approaches for AI systems.
• Experience with model fine-tuning, quantization, evaluation, model routing, inference optimization, or deployment of open-source and proprietary models.
• Experience in AI governance, data privacy, security architecture, responsible AI, compliance, model risk, observability, and production AI operations.
• Relevant architecture, cloud, or AI certifications are welcome but not required.

Core Capabilities

Beyond the qualifications above, success in this role depends on:

Technical communication. Explaining architecture and trade-offs credibly at client executive and enterprise-architecture level, as well as to engineering teams.

Influence without authority. Driving technical decisions across client and internal organizations where you hold no formal reporting line.

Developing others. Mentoring senior architects and advisors, leading architecture review forums, and raising the technical bar across the practice.

Judgment under ambiguity. Choosing fit-for-purpose approaches when data readiness, commercial constraints, and client appetite conflict, including recommending against AI where it isn’t warranted.

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.

Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form, https://us.nttdata.com/en/contact-us.

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