Hands-On AI Architect for Enterprise GenAI

NTT DATA, Inc.

Charlotte (NC)

On-site

USD 140,000 - 210,000

Full time

14 days+

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Job summary

NTT DATA, Inc. seeks an AI Architect at Lead Consultant level in Charlotte, NC to lead AI projects from discovery to deployment. You will design major AI components, drive technical leadership, and mentor teams across data, infrastructure, and security functions.

Hands-on work will be ~70% with design and leadership ~30% of the time. The role spans ML, NLP, CV, and GenAI, with a focus on secure-by-design practices, data governance, and resilient, scalable AI platforms in multi-cloud and on-prem

Qualifications

Bachelor’s degree in computer science, engineering, data science, information systems, or a related technical discipline.
8+ years in software engineering, cloud, data engineering, AI/ML, architecture, or enterprise technology delivery.
4+ years designing and delivering production AI or ML solutions.
1+ years delivering production Generative AI or LLM-based systems, including LLM APIs, RAG, embeddings, vector and graph-based retrieval, evaluation, guardrails, and agentic workflows.
5+ years in software engineering with professional-level proficiency in at least one mainstream language — Python, Java, C#, C++, TypeScript/JavaScript, Go, or equivalent.
3+ years delivering technical design and implementation of significant components in complex enterprise environments.
2+ years architecting AI solutions across more than one deployment model and establishing operational practice for AI systems using CI/CD and DevOps tooling.

Responsibilities

Lead AI projects from conception through deployment, owning design and delivery for the workstreams you are accountable for.
Translate business challenges into AI solution designs, and evaluate the business impact and expected return of the approaches you propose.
Design and implement major components of enterprise AI solutions across applications, integration, data, AI/ML, and infrastructure.
Design and build AI solution patterns across ML and GenAI — LLM applications, retrieval-augmented generation, agents, orchestration, embeddings, vector and graph-based retrieval, model integrations, and evaluation.
Conduct data preprocessing, feature engineering, and dataset preparation for AI workloads.
Build prototypes, proofs of concept, reference implementations, and production‑ready AI services.
Optimize AI models and inference workloads for performance, scalability, and cost, including on distributed computing frameworks and AI‑optimized hardware.
Design data storage and retrieval approaches for AI workloads across databases, data lakes, and vector or graph stores.
Apply secure‑by‑design practices — authentication, RBAC, secrets management, encryption, private networking, data protection, auditability, and authorization‑aware data retrieval.
Implement responsible‑AI controls, including content safety, PII protection, bias identification and mitigation, prompt‑injection defenses, and human review.
Work within client data governance requirements and applicable data privacy regulation in everything you design and deliver.
Apply MLOps, LLMOps, and GenAIOps practices — CI/CD, version control, model and prompt versioning, infrastructure as code, testing, evaluation, monitoring, tracing, and lifecycle management.
Communicate AI concepts, design decisions, and trade‑offs clearly to client technical, product, and business teams, and collaborate across delivery, data, infrastructure, and security functions to integrate AI solutions.
Mentor engineers and junior team members in AI engineering and architecture practices.
Contribute to reusable patterns, accelerators, and delivery playbooks; stay current with emerging AI technologies; and provide technical input to discovery workshops, solution shaping, and effort estimates.