Job description

Indent: SF_OP_208146-1 / SF_OP_208146-2 Role: AI Architect Location: Charlotte NC (3 days Work from Office) Max Rate: $78/hr 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

Requirements

Must-haves

Indent: SF_OP_208146-1 / SF_OP_208146-2
Role: AI Architect
Location: Charlotte NC (3 days Work from Office)
Max Rate: $78/hr
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

Nice-to-haves

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.

Application prep

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Keywords to mirror

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