AI Architect - Hybrid in Charlotte, NC

NTT DATA Services•Charlotte, NC

1d•$102,675 - $165,799•Hybrid

About The Position

We are seeking an experienced AI Architect to design and lead the development of enterprise-scale Artificial Intelligence and Generative AI solutions. This role will define AI architecture, technology strategy, and implementation patterns while helping organizations move AI initiatives from proof of concept to secure, scalable production solutions. The ideal candidate combines strong enterprise architecture experience with hands-on knowledge of LLMs, Generative AI, RAG, AI agents, cloud platforms, AI/ML services, and enterprise integration. You will work closely with business leaders, data scientists, ML engineers, software developers, security teams, and enterprise architects to turn emerging AI capabilities into practical business solutions.

Requirements

10+ years of experience in software, technology, solution, or enterprise architecture.
5+ years of experience designing and implementing AI/ML or advanced analytics solutions.
3+ years of hands-on experience with Generative AI and LLM-based solutions.
3+ years of experience with one or more major cloud platforms: AWS, Azure, or GCP.
Strong Python development experience and familiarity with modern AI/ML frameworks.
Hands-on experience designing RAG, vector search, embeddings, and knowledge-retrieval solutions.
Experience integrating AI and LLM services through REST APIs and enterprise applications.
Experience with enterprise application integration, APIs, databases, and distributed systems.
Experience with AI security, governance, responsible AI, data privacy, and/or model-risk practices.
Proven experience leading AI PoCs, prototypes, and/or production implementations.
Strong experience working across engineering, data, product, architecture, security, and business teams.
Excellent communication and stakeholder-management skills.
Bachelor's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related discipline.
Equivalent professional experience and demonstrated expertise in enterprise AI architecture may be considered.

Nice To Haves

Experience designing AI agents and agentic workflows.
Experience with platforms and services such as: Azure OpenAI, AWS Bedrock, Google Vertex AI, OpenAI, Anthropic.
Experience with LangChain, LangGraph, LlamaIndex, or comparable AI application frameworks.
Experience with vector databases and search technologies such as Pinecone, Azure AI Search, OpenSearch, or similar platforms.
Experience with MLOps / LLMOps, including model deployment, monitoring, evaluation, observability, and lifecycle management.
Experience with AI evaluation frameworks and LLM performance optimization.
Experience implementing enterprise-scale AI modernization and transformation initiatives.
Experience in financial services, insurance, healthcare, or other regulated industries.
Familiarity with cloud-native architecture, microservices, APIs, containers, and distributed systems.

Responsibilities

Design and lead AI and Generative AI architectures for enterprise applications and business solutions.
Define AI technology strategies, reference architectures, standards, and implementation roadmaps.
Translate business requirements into secure, scalable, and production-ready AI solutions.
Evaluate and recommend AI models, platforms, frameworks, and cloud services based on business and technical requirements.
Establish architecture patterns that support scalability, reliability, performance, security, and cost efficiency.
Guide AI initiatives from initial concept and PoC through production implementation.
Design solutions leveraging LLMs, Generative AI, RAG, prompt engineering, AI agents, and AI/ML services.
Architect retrieval-augmented generation (RAG) solutions, including: Vector search, Embeddings, Knowledge retrieval, Document processing, Knowledge bases, Context management.
Evaluate LLM performance, accuracy, scalability, latency, and cost.
Design approaches for prompt engineering, model selection, evaluation, and continuous improvement.
Identify opportunities to apply AI and automation to complex enterprise business processes.
Design secure and scalable AI solutions across AWS, Azure, and/or Google Cloud Platform (GCP).
Integrate AI capabilities with enterprise applications, APIs, databases, data platforms, and distributed systems.
Design REST API and service-integration patterns for AI/LLM capabilities.
Evaluate cloud-native AI/ML services and determine the appropriate architecture for enterprise use cases.
Ensure AI solutions integrate effectively with existing enterprise technology ecosystems.
Establish architecture standards for AI security, governance, privacy, and responsible AI.
Incorporate data protection, access controls, security, and compliance requirements into AI architectures.
Support model-risk management and appropriate governance for enterprise AI applications.
Define controls for AI solution monitoring, evaluation, auditability, and operational risk.
Partner with security, risk, privacy, and compliance teams to ensure solutions meet enterprise and regulatory requirements.
Lead AI proofs of concept, prototypes, and technical evaluations.
Assess emerging AI technologies and determine their practical enterprise applications.
Establish criteria for moving AI solutions from experimentation to production.
Define requirements for performance, scalability, reliability, monitoring, observability, and cost management.
Help establish repeatable architecture patterns and best practices for enterprise AI adoption.
Provide technical direction to AI/ML engineers, software developers, data scientists, and architecture teams.
Mentor engineering teams on AI architecture, implementation patterns, and best practices.
Lead architecture reviews and communicate technical decisions to both technical and non-technical stakeholders.
Collaborate with product, engineering, data, security, infrastructure, and business teams.
Clearly communicate complex AI concepts, technical trade-offs, and recommendations to senior stakeholders.