AI Architect

TQL • Charlotte, NC

159d • Onsite

About The Position

As an AI Architect at TQL, you will define and lead the enterprise-wide AI architecture that powers next-generation intelligence and automation across our logistics and freight brokerage ecosystem. This role is responsible for setting how AI systems are designed, built, governed and scaled – ensuring solutions are secure, reliable, cost-efficient and deeply embedded into business workflows.

You will partner closely with Engineering, Product Management, Data and Operations leadership to identify high-impact use cases and deliver AI capabilities that drive measurable improvements across pricing, capacity matching, customer service, claims, risk and operator productivity.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, AI/ML or a related field
• 7–12+ years of experience in AI/ML engineering, cloud architecture or enterprise software engineering
• Proven experience architecting and delivering production AI or ML solutions on Azure
• Experience with REST APIs, serverless functions, microservices and event-driven architectures
• Backend development in Python with working knowledge of C# or Node.js.
• Hands-on experience with Azure OpenAI, Azure Machine Learning, Azure AI Search, Microsoft Fabric and Lakehouse architectures
• Experience with embeddings, vector databases, RAG patterns, LangChain, Semantic Kernel and MLflow
• Proficiency with Git, Azure DevOps CI/CD, Docker and Kubernetes
• Strong understanding of data modeling, governance, lineage and security
• Strong communication skills across technical and non-technical audiences
• Ability to translate business workflows into scalable technical architectures
• Strong ownership mindset with focus on reliability, cost optimization and long-term scalability
• Product mindset with ability to align AI architecture to business outcomes

Nice To Haves

• Azure certifications (Solutions Architect, Azure AI Engineer) preferred

Responsibilities

• Evaluate and recommend AI models, APIs and platforms (e.g., Anthropic, OpenAI, Microsoft, Google) based on security, reliability, cost and enterprise fit
• Define the enterprise AI architecture across Azure OpenAI, Azure AI Search, Microsoft Fabric, Azure ML, APIs, event-driven systems and operator-facing tools
• Establish standards for building LLM applications, retrieval-augmented generation (RAG) systems, intelligent agents and ML models at scale
• Create reference architectures for AI-powered solutions including real-time workflows, automation, copilots and knowledge assistants
• Design how AI services integrate with core applications, including broker tools, APIs, workflows and backend services
• Establish patterns for serverless functions, microservices, REST APIs, event-driven pipelines and end-to-end orchestration
• Partner with application development teams to embed AI into product features with the right performance, security, authentication and data flow patterns
• Ensure AI solutions meet enterprise CI/CD, observability, reliability and SLA standards
• Lead solution designs for AI platforms including vector databases, embedding pipelines, inference services, feature stores and model registries
• Translate complex operator workflows into scalable, AI-enabled architectures that improve decision-making and productivity
• Conduct architecture, design reviews and mentor AI Engineers, Software Engineers, Data Engineers and Data Scientists
• Partner with Data Engineering to ensure Fabric Lakehouse, Delta tables, warehouse layers and streaming systems support both training and inference workloads
• Architect and optimize RAG pipelines using Azure AI Search, vector indexing, embeddings and metadata strategies
• Define and implement enterprise MLOps standards for model lifecycle management, versioning, monitoring and retraining
• Apply Responsible AI practices including content filtering, privacy, compliance and hallucination mitigation
• Ensure AI systems are observable with performance and cost monitoring
• Evaluate emerging AI models, agent frameworks and Azure capabilities for use in logistics workflows
• Lead proofs of concept (PoCs) and accelerate adoption of high-value AI initiatives
• Develop reusable technical playbooks and architectural patterns to mature AI across engineering teams

Benefits

• Competitive compensation
• Opportunity to influence enterprise‑wide AI architecture
• High visibility partnership with executive leadership
• Long‑term career growth in a collaborative, AI‑driven organization
• Comprehensive benefits package
• Health, dental and vision coverage
• 401(k) with company match
• Perks including employee discounts, financial wellness planning, tuition reimbursement and more

What This Job Offers

• Job Type: Full-time
• Career Level: Senior