Lead AI Architect

Lead AI Architect

Location: Charlotte, NC - Onsite



Employment Type: Contract - W2 only (No C2C)

Role Overview

We are seeking two experienced Lead Architects on a contract basis to strengthen delivery of architecture work across the technology portfolio. These roles operate at the Lead Architect level and partner closely with the internal architecture team, engineering leads, and business stakeholders.

The right candidates are hands-on, AI-fluent architects who can quickly translate business and technical needs into clear architecture artifacts and stand up working prototypes to prove out ideas fast. They should be comfortable moving between big-picture design and rapid, tangible experimentation.

Key Responsibilities

• Assemble architecture designs - produce clear, end-to-end architecture artifacts: solution designs, reference architectures, diagrams, patterns, and architecture decision records.
• Apply AI capabilities - design and integrate AI / GenAI / machine-learning components into solutions, and advise on appropriate patterns, tooling, and trade-offs.
• Drive rapid prototypes - build quick proofs of concept and working prototypes to validate architecture decisions, de-risk options, and accelerate stakeholder alignment.
• Partner across teams - work with engineering, product, and business partners to align architecture with delivery goals and non-functional requirements (security, scalability, resilience, cost).
• Document and communicate - present designs and trade-offs clearly to both technical and non-technical audiences, and keep architecture documentation current and usable.
• Uphold standards - apply enterprise architecture standards, controls, and best practices throughout design and prototyping.

Required Qualifications (Must-Have)

• Demonstrable hands-on AI experience - practical work building, integrating, or architecting AI / GenAI / ML solutions.
• Proven ability to put architecture details together - a track record of producing clear, complete architecture designs and documentation.
• Ability to drive quick prototypes - rapidly stand up POCs and working prototypes to test and demonstrate ideas.
• Senior, hands-on technical background operating at a Lead Architect level, with strong design fundamentals across modern application, integration, and cloud patterns.
• Strong communication skills - able to explain architecture and trade-offs to technical teams and business stakeholders alike.
• Self-directed and comfortable working across ambiguity to deliver tangible outcomes quickly.

Technical Skills

Candidates should bring strong depth across several of the following areas:

• AI & Machine Learning - GenAI and large language models (LLMs), retrieval-augmented generation (RAG), agentic and prompt-engineering patterns, model APIs and integration, embeddings and vector stores; familiarity with common ML frameworks.
• Cloud & Platform - hands-on experience with at least one major cloud (AWS, Azure, or GCP); containers and orchestration (Docker, Kubernetes); serverless services.
• Architecture & Integration - microservices, event-driven and API-led design, REST / GraphQL APIs, messaging and streaming (e.g., Kafka), and enterprise integration patterns.
• Data & Information Architecture - data modeling, relational and NoSQL databases (strong SQL), data lakes / warehouses, ETL / ELT pipelines, and data governance / metadata.
• Languages & Prototyping - proficiency in Python and/or Java (or comparable); rapid prototyping, scripting, and notebook-based experimentation.
• Engineering Practices - CI/CD, infrastructure as code (e.g., Terraform), Git-based version control, and automated testing.
• Architecture Tooling - modeling and diagramming (C4, UML, or ArchiMate) and architecture decision records (ADRs).

Preferred Qualifications (Nice-to-Have)

• Depth in data / information architecture - experience designing data models, information flows, data platforms, or enterprise information architecture.
• Finance domain experience - prior work in Finance functions, especially Controllers / financial control and related processes.

What Success Looks Like

• Architecture artifacts and prototypes are delivered quickly and are clear enough to drive decisions.
• AI options are evaluated and applied pragmatically, with sound trade-off analysis.
• Stakeholders trust the designs and can act on them with minimal rework.