Generative AI Architect

NLB Services; Raleigh (NC); On-site; USD 150,000 - 230,000; Full time

Job summary

NLB Services in Raleigh, NC seeks a Generative AI Architect to lead enterprise AI initiatives. The role bridges AI platforms, engineering, product, and business stakeholders, delivering hands-on guidance and reusable AI frameworks to accelerate adoption.

The candidate will influence decisions across delivery teams, implement AI solutions using LLMs, RAG and AI agents, and shape governance, security, and architectural standards for scalable, responsible AI across the enterprise.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, software engineering, data science or related field.
• 8+ years of technology experience, with 3+ years hands-on AI experience.
• Strong hands-on experience with Generative AI, LLMs, Agentic AI, RAG, and semantic search.
• Experience designing enterprise solutions on AWS, Azure or GCP.
• Experience with AI engineering frameworks such as LangChain/LangGraph/Semantic Kernel.

Responsibilities

• Drive enterprise-wide adoption of Generative AI and AI-native tech through technical leadership.
• Establish AI engineering standards, reference architectures, reusable frameworks and patterns.
• Enable teams to design, deploy, and operate AI-powered applications and automation.
• Provide guidance on AI solution architecture, model selection, orchestration, and RAG.
• Develop accelerators, templates, proof-of-concepts and onboarding assets.
• Define AI platform capabilities, governance, security and operations practices.
• Guide teams on AI technologies including embeddings, vector databases, semantic search and cloud services.
• Advance Responsible AI with governance, guardrails and risk management.

Skills

Generative AI

LLMs

RAG

AI orchestration

Vector databases

Enterprise AI architecture

Education

Bachelor's or Master's in CS/AI/SE/Data Science

Tools

LangChain

LangGraph

Semantic Kernel

CrewAI

AutoGen

AWS/Azure/GCP

Job description

We are seeking a Generative AI Architect / AI Enabler (Technical) to provide senior technical leadership in accelerating enterprise adoption of Generative AI, Agentic AI, Machine Learning, and AI-native technologies.

This role will serve as a bridge between AI platform teams, engineering teams, architects, product owners, and business stakeholders. The ideal candidate will provide hands‑on technical guidance, establish reusable AI engineering frameworks and reference architectures, and enable teams to successfully design, build, deploy, and operationalize enterprise AI solutions.

The role requires strong expertise in LLMs, RAG, AI agents, AI orchestration, vector databases, cloud AI services, and enterprise AI architecture, along with the ability to influence technical decisions across multiple delivery teams.

Key Responsibilities

• Drive enterprise-wide adoption of Generative AI, Agentic AI, Machine Learning, and AI-native technologies through technical leadership and implementation guidance.
• Establish and promote AI engineering standards, reference architectures, reusable frameworks, design patterns, and best practices.
• Enable engineering and product teams to design, develop, deploy, and operate AI‑powered applications and intelligent automation solutions.
• Provide technical guidance on AI solution architecture, model selection, orchestration frameworks, agent design, RAG implementations, and enterprise system integration.
• Develop reusable accelerators, templates, proof‑of‑concepts, sample applications, and onboarding assets to accelerate AI adoption and developer productivity.
• Partner with platform engineering teams to define AI platform capabilities, infrastructure standards, governance policies, security controls, and operational practices.
• Guide teams in implementing modern AI technologies, including LLMs, embeddings, vector databases, semantic search, AI agents, orchestration frameworks, and cloud AI services.
• Enable Responsible AI through governance, guardrails, security controls, compliance requirements, and AI risk‑mitigation practices.
• Lead technical workshops, architecture reviews, design sessions, hackathons, and knowledge‑sharing initiatives.
• Collaborate with technology vendors and partners to evaluate emerging AI technologies, frameworks, and enterprise AI platforms.

Primary Technical Responsibilities

• Act as the primary technical advisor for enterprise AI initiatives and translate business objectives into scalable, secure, and supportable AI solutions.
• Guide engineering teams through end‑to‑end implementation of RAG pipelines, AI copilots, multi‑agent systems, intelligent workflows, and enterprise AI platforms.
• Develop and maintain enterprise AI reference architectures, technical standards, implementation guidelines, and onboarding documentation.
• Facilitate adoption of AI engineering tools, frameworks, cloud services, and modern development practices.
• Conduct architecture reviews and technical assessments to ensure solutions meet enterprise standards for security, scalability, performance, and Responsible AI.
• Identify technology gaps, implementation challenges, and operational risks and recommend solutions to accelerate AI delivery.
• Establish AI communities of practice, technical learning programs, and hands‑on enablement workshops.
• Partner with leadership to define AI adoption roadmaps, platform strategies, and technology investment priorities.
• Define and track AI adoption metrics, platform utilization, technical enablement outcomes, and business impact.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, Information Systems, or a related technical field.
• 8+ years of overall technology experience, including 3+ years of hands‑on AI‑related experience.
• Strong hands‑on experience with Generative AI, LLMs, Agentic AI, RAG, semantic search, and modern AI application architectures.
• Experience designing and implementing enterprise solutions using AWS, Azure, or Google Cloud Platform (GCP).
• Experience with modern AI/agent frameworks such as:
• LangChain
• LangGraph
• Semantic Kernel
• CrewAI
• AutoGen
• Google ADK
• Or equivalent AI engineering frameworks
• Strong understanding of:
• Embeddings
• AI orchestration
• Experience leading technical design reviews, architecture governance, technology enablement, and enterprise modernization initiatives.
• Strong understanding of software engineering practices, including APIs, microservices, cloud‑native architectures, CI/CD, observability, security, and platform engineering.
• Excellent communication and stakeholder management skills, with the ability to explain complex AI concepts to both technical and non‑technical audiences.

Preferred Qualifications

• Experience building or enabling enterprise AI platforms, AI Centers of Excellence (CoEs), or large‑scale AI transformation programs.
• Experience within healthcare, insurance, financial services, or other highly regulated industries.
• Understanding of regulatory and compliance considerations involving PHI, PII, HIPAA, and enterprise data security.
• Knowledge of Responsible AI frameworks, AI governance, AI risk management, and compliance standards.
• Hands‑on experience with AI observability, evaluation frameworks, model monitoring, and AI lifecycle management.
• Experience supporting AI strategy, technology roadmaps, platform adoption, and executive‑level AI initiatives.
• Demonstrated ability to influence architecture decisions, build cross‑functional partnerships, and drive enterprise‑scale AI adoption.

Core Skills

Generative AI | LLMs | Agentic AI | AI Agents | RAG | Python | LangChain | LangGraph | Semantic Kernel | CrewAI | AutoGen | LlamaIndex | Vector Databases | Embeddings | Semantic Search | Prompt Engineering | AWS | Azure | GCP | AI Architecture | Enterprise AI | AI Governance | Responsible AI | AI Platforms | Cloud‑Native Architecture | APIs | Microservices | CI/CD | Observability