Recently postedTechnology

AI Architect @Charlotte NC

e-IT Professionals Corp.Charlotte, NC🇺🇸United States

Quick overview

SeniorityMid Senior

Work modeOn Site

LocationCharlotte, NC, United States

DjangoDockerFastAPIFlaskMicroservicesMongoDBSQLGitAWSSnowflakeDatabricksGitHub ActionsGoogle CloudJenkinsLLMPythonTerraformGenerative AIRESTAzurePostgreSQLKubernetesSQL ServerPrompt EngineeringCI/CDAzure DevOpsLangChainLangGraphRAG

Job description

Role: AI Architect

Location: Charlotte NC (3 days Work from Office)

About the Role

We are seeking a highly experienced AI Architect to lead the design, strategy, and implementation of enterprise-scale AI solutions powered by Agentic AI, LLMs/SLMs, Autonomous Agents, and Generative AI platforms. The role will focus on architecting multi-agent systems, supervisory AI workflows with human-in-the-loop controls, evaluating agentic skills, design integration, workflows, and driving AI-led transformation across data and modernization initiatives. This role requires strong collaboration with business and technology stakeholders to translate AI innovation into scalable, governed, and business-aligned solutions.

Key Responsibilities

Define architecture and design principles, support planning of technical implementation roadmaps.
Partner with client stakeholders, Product Owner to identify, prioritize, and shape AI use cases aligned to business and data transformation goals.
Architect autonomous agent platforms with supervisory controls, human-in-the-loop workflows, governance, and auditability.
Design multi-agent systems leveraging planning, reasoning, memory, orchestration, and tool-calling capabilities.
Define agent communication and interoperability frameworks using MCP (Model Context Protocol) and modern AI integration patterns.
Evaluate and recommend optimal hybrid strategy for LLM/SLM and deterministic approach for enterprise AI applications, automation platforms, and transpiler solutions.
Research emerging AI technologies, frameworks, and industry trends to provide strategic recommendations.
Develop AI governance, safety, observability, security, and responsible AI frameworks.
Define agent mapping, policy recommendations, and operating models for enterprise AI adoption.
Collaborate with engineering, product, and business teams to establish reusable AI architecture patterns and standards.
Support executive stakeholders through architecture reviews, technology assessments, workshops, and strategic advisory engagements.

Required Skills & Expertise

Agentic AI & Autonomous Systems

Multi-Agent Architectures
Agentic AI Workflows
Autonomous Agent Platforms
Human-in-the-Loop AI Systems
Supervisory AI Architectures
Agent Planning, Reasoning, and Memory
Tool Calling & Function Calling
Agent Governance and Guardrails
MCP (Model Context Protocol)

Generative AI & Foundation Models

OpenAI Models
Anthropic Claude
Azure OpenAI
Gemini
Llama Models
Large Language Models (LLMs)
Small Language Models (SLMs)
Prompt Engineering
Model Evaluation & Benchmarking
AI Safety and Responsible AI

AI Engineering & Platform Architecture

Python
FastAPI and API-Driven Architectures
Distributed Systems
Cloud-Native AI Platforms
RAG Architectures
Vector Databases and Semantic Search
AI Observability and Monitoring

Cloud & Enterprise Platforms

Microsoft Azure
AWS
Google Cloud Platform
Docker
Kubernetes
Terraform
CI/CD and DevOps Practices

AI Tools & Developer Productivity

Hands-on proficiency with modern AI-assisted engineering tools including:

Microsoft Copilot
GitHub Copilot
Devin
Claude Code
AI Coding Agents
AI-Powered Development Workflows

Knowledge & Retrieval Systems

Retrieval Augmented Generation (RAG)
Semantic Search
Vector Search
Hybrid Retrieval
Knowledge Graphs
Embeddings
Re-ranking Models
Context Engineering

Preferred Qualifications

Experience architecting enterprise AI platforms in regulated environments.
Experience designing supervisory AI systems with governance and compliance controls.
Strong understanding of AI risk management, explainability, and policy frameworks.
Experience with AI-driven modernization, code conversion, transpilers, and automation platforms.
Knowledge of Data Engineering, Data Governance, Data Quality, Metadata Management, and Analytics ecosystems.
Cloud Architecture certifications (Azure, AWS, or Google Cloud Platform) preferred.

RAG & Knowledge Intelligence

Architect enterprise-scale Retrieval-Augmented Generation (RAG) solutions.
Design hybrid retrieval systems using embeddings, vector search, semantic search, knowledge graphs, and reranking models.
Build scalable pipelines for document intelligence, metadata discovery, semantic mapping, and contextual reasoning.
Optimize retrieval quality, latency, grounding, and hallucination mitigation.

Platform Engineering

Architect cloud-native microservices using Python and FastAPI.
Design distributed event-driven architectures leveraging APIs, messaging systems, and asynchronous processing.
Lead implementation of CI/CD, Infrastructure as Code, observability, monitoring, and platform security.
Ensure production readiness, resiliency, scalability, and cost optimization.

Stakeholder & Team Leadership

Collaborate with Product Owners, Data Architects, Engineering Teams, Data Scientists, and Business Stakeholders.
Mentor senior engineers and AI developers on architecture best practices.
Drive technical decision-making, architecture reviews, and innovation initiatives.
Support customer engagements, solution workshops, and executive presentations.

Required Technical Skills

Core Engineering