We are currently seeking a AI Architect to join our team in Charlotte, North Carolina (US-NC), United States (US).

Role Summary

As an AI Architect at Lead Consultant level, you will lead AI projects from conception through deployment for clients across industries, translating business challenges into working AI solutions. You will design and build the major components of those solutions, and act as the technical lead for the workstreams you own within a client engagement.

The role spans the AI lifecycle - from discovery and solution design through proof of concept, production implementation, and operational improvement. The scope includes machine learning, predictive analytics, natural language processing, computer vision, intelligent automation, and Generative AI.

This is a hands-on architecture role. Expect roughly 70% of your time hands-on - building reference implementations, prototypes, production services, and integration work - and roughly 30% on design, technical leadership, and mentoring. Engagement models vary, and some client contexts require sustained hands-on delivery alongside the engineering team.

Key Responsibilities

• Lead assigned AI projects from conception through deployment, owning design and delivery for the workstreams you are accountable for.
• Translate business challenges into AI solution designs, and evaluate the business impact and expected return of the approaches you propose.
• Design and implement major components of enterprise AI solutions across applications, integration, data, AI/ML, and infrastructure.
• Design and build AI solution patterns across ML and GenAI - LLM applications, retrieval-augmented generation, agents, orchestration, embeddings, vector and graph-based retrieval, model integrations, and evaluation.
• Conduct data preprocessing, feature engineering, and dataset preparation for AI workloads.
• Build prototypes, proofs of concept, reference implementations, and production-ready AI services.
• Optimize AI models and inference workloads for performance, scalability, and cost, including on distributed computing frameworks and AI-optimized hardware.
• Design data storage and retrieval approaches for AI workloads across databases, data lakes, and vector or graph stores.
• Apply secure-by-design practices - authentication, RBAC, secrets management, encryption, private networking, data protection, auditability, and authorization-aware data retrieval.
• Implement responsible-AI controls, including content safety, PII protection, bias identification and mitigation, prompt-injection defenses, and human review.
• Work within client data governance requirements and applicable data privacy regulation in everything you design and deliver.
• Apply MLOps, LLMOps, and GenAIOps practices - CI/CD, version control, model and prompt versioning, infrastructure as code, testing, evaluation, monitoring, tracing, and lifecycle management.
• Communicate AI concepts, design decisions, and trade-offs clearly to client technical, product, and business teams, and collaborate across delivery, data, infrastructure, and security functions to integrate AI solutions.
• Mentor engineers and junior team members in AI engineering and architecture practices.
• Contribute to reusable patterns, accelerators, and delivery playbooks; stay current with emerging AI technologies; and provide technical input to discovery workshops, solution shaping, and effort estimates.

Basic Qualifications

The experience periods below overlap and are not additive.

• Bachelor's degree in computer science, engineering, data science, information systems, or a related technical discipline.
• 8+ years in software engineering, cloud, data engineering, AI/ML, architecture, or enterprise technology delivery.
• 4+ years designing and delivering production AI or ML solutions.
• 1+ years delivering production Generative AI or LLM-based systems, including LLM APIs, RAG, embeddings, vector and graph-based retrieval, evaluation, guardrails, and agentic workflows.
• 5+ years in software engineering with professional-level proficiency in at least one mainstream language - Python, Java, C#, C++, TypeScript/JavaScript, Go, or equivalent - including designing and integrating APIs and backend services using REST, gRPC, event-driven, or asynchronous patterns.
• 3+ years delivering technical design and implementation of significant components in complex enterprise environments.
• 2+ years architecting AI solutions across more than one deployment model (public cloud plus private cloud, hybrid, or on-premises) and establishing operational practice for AI systems - model and prompt versioning, artifact and experiment tracking, automated evaluation, monitoring and tracing, incident response, rollback - using CI/CD, infrastructure as code, and DevOps tooling.

Preferred Qualifications

• Master's degree in AI/ML, computer science, engineering, data science, or a related field.
• 2+ years in consulting or professional services delivering technical solutions to external clients, including discovery workshops, solution shaping, effort estimation, and proposal input.
• 2+ years architecting secure enterprise systems - identity and authentication, authorization and RBAC, secrets management, encryption, network isolation, audit logging - and implementing responsible-AI controls in production, including content safety, PII protection, prompt-injection defenses, human review, and model risk management.
• Experience spanning Generative AI and one or more additional AI domains, such as classical ML, predictive analytics, NLP, computer vision, or intelligent automation.
• Experience across two or more major cloud AI platforms, including their managed model, retrieval, and orchestration services.
• Experience with container orchestration and GPU or accelerator infrastructure.
• Experience with model-serving and inference-optimization stacks, open-source or vendor, for self-hosted and private deployments.
• Experience with modern deep learning and classical ML frameworks, and the wider open-source model ecosystem.
• Experience with knowledge graphs, ontologies, semantic modelling, and graph databases, including graph-based or hybrid retrieval approaches for AI systems.
• Experience with model fine-tuning, quantization, evaluation, model routing, inference optimization, or deployment of open-source and proprietary models.
• Experience in AI governance, data privacy, security architecture, responsible AI, compliance, model risk, observability, and production AI operations.
• Relevant architecture, cloud, or AI certifications are welcome but not required.

Core Capabilities

Beyond the qualifications above, success in this role depends on:

Technical communication. Explaining design decisions and trade-offs clearly to delivery teams and client technical staff.

Influence. Building consensus within a delivery team and bringing others with you on technical decisions.

Developing others. Mentoring engineers and junior team members, and raising the standard of the work around you.

Judgment under ambiguity. Recognising which decisions need to be escalated and framing them well when they are.