AI Architect Lead Advisor

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, includ