AI Architect

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

• Expert-level Python development
• FastAPI, Flask, Django
• REST APIs and Microservices Architecture
• Async Programming (asyncio)
• Design Patterns and Clean Architecture
• Distributed Systems Architecture

Agentic AI

• LangGraph
• LangChain
• CrewAI
• AutoGen
• LlamaIndex
• MCP (Model Context Protocol)
• Multi-Agent Orchestration
• Tool Calling and Function Calling
• Agent Memory and Planning Frameworks

Generative AI

• OpenAI
• Azure OpenAI
• Claude
• Gemini
• Llama Models
• Prompt Engineering
• Fine-tuning and Model Evaluation
• AI Safety and Guardrails

RAG & Search

• Retrieval-Augmented Generation (RAG)
• Vector Search Architecture
• Semantic Search
• Hybrid Search
• Embeddings
• Re-ranking Models
• Knowledge Graph Integration

Vector Databases

• Pinecone
• ChromaDB
• Qdrant
• Milvus
• Weaviate
• pgVector

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform

Data & Databases

• PostgreSQL
• MongoDB
• SQL Server
• Snowflake
• Databricks
• Data Lake Architectures

DevOps & Platform Engineering

• Docker
• Kubernetes
• OpenShift
• Terraform
• GitHub Actions
• Azure DevOps
• Jenkins
• OpenTelemetry