Senior AI Architect & Strategy Lead

NTT DATA North America

Plano (TX)

Hybrid

USD 180,000 - 240,000

Full time

9 hours ago

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Job summary

NTT DATA North America is seeking an AI Architect at Lead Advisor level to drive large-scale AI programs across multiple client engagements. You will shape the AI vision and multi-year roadmaps, set architectural standards, and serve as the senior technical authority, guiding design and governance across data, AI/ML, security, and operations.

The role combines hands-on delivery with architecture leadership, including reference implementations, design governance, and mentoring.

Qualifications

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.

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.