AI Architect with Python FASTAPI (Charlotte NC) (3 days Work from Office)

Role: AI Architect Location: Charlotte NC (3 days Work from Office) Long Term 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 GCP) 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 •