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Total Quality Logistics

Freight brokerage linking shippers to carriers

AI Architect

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeCharlotte, NC, USANo salary listedIn-person

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science, Data Science, Engineering, AI/ML, or a related field is required.
  • Seven to twelve or more years of experience in AI/ML engineering, cloud architecture, or enterprise software engineering is required.
  • Proven experience architecting and delivering production AI or machine learning solutions on Azure is required.
  • Experience with REST APIs, serverless functions, microservices, and event-driven architectures is required.
  • Backend development experience in Python and working knowledge of C# or Node.js are required.
  • Hands-on experience with Azure OpenAI, Azure Machine Learning, Azure AI Search, Microsoft Fabric, and Lakehouse architectures is required.
  • Experience with embeddings, vector databases, retrieval-augmented generation patterns, LangChain, Semantic Kernel, and MLflow is required.
  • Proficiency with Git, Azure DevOps continuous integration and continuous delivery, Docker, and Kubernetes is required.
  • A strong understanding of data modeling, governance, lineage, and security is required.
  • Strong communication skills across technical and non-technical audiences are required.
  • The ability to translate business workflows into scalable technical architectures is required.
  • A strong ownership mindset focused on reliability, cost optimization, and long-term scalability is required.
  • A product mindset and the ability to align AI architecture with business outcomes are required.

Responsibilities

  • Define and lead enterprise-wide AI architecture for intelligence and automation across the logistics and freight brokerage ecosystem.
  • Partner with Engineering, Product Management, Data, and Operations leadership to identify high-impact use cases and deliver AI capabilities that improve pricing, capacity matching, customer service, claims, risk, and operator productivity.
  • Evaluate and recommend AI models, application programming interfaces, and platforms based on security, reliability, cost, and enterprise fit.
  • Define enterprise AI architecture across Azure OpenAI, Azure AI Search, Microsoft Fabric, Azure Machine Learning, APIs, event-driven systems, and operator-facing tools.
  • Establish standards for building large language model applications, retrieval-augmented generation systems, intelligent agents, and machine learning models at scale.
  • Create reference architectures for AI-powered solutions, including real-time workflows, automation, copilots, and knowledge assistants.
  • Design integrations between AI services and 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 using appropriate performance, security, authentication, and data-flow patterns.
  • Ensure AI solutions meet enterprise continuous integration and continuous delivery, observability, reliability, and service-level agreement standards.
  • Lead solution designs for AI platforms, including vector databases, embedding pipelines, inference services, feature stores, and model registries.
  • Translate operator workflows into scalable, AI-enabled architectures that improve decision-making and productivity.
  • Conduct architecture and 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 training and inference workloads.
  • Architect and optimize retrieval-augmented generation 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 through performance and cost monitoring.
  • Evaluate emerging AI models, agent frameworks, and Azure capabilities for logistics workflows.
  • Lead proofs of concept and accelerate adoption of high-value AI initiatives.
  • Develop reusable technical playbooks and architectural patterns to mature AI across engineering teams.

Desired Qualifications

  • Azure certifications such as Solutions Architect or Azure AI Engineer are preferred.

About the Company

Total Quality Logistics

Freight brokerage linking shippers to carriers

Total Quality Logistics (TQL) acts as a freight broker that connects shippers needing truckload transport with vetted carriers that have the capacity to move it. Its service operates around the clock, 24/7/365, with sales and support teams handling accounting, after-hours calls, and special handling to ensure shipments move smoothly. TQL differentiates itself through its large scale in the $500 billion logistics market, an extensive network of carriers, around-the-clock support, and a transparent, career-oriented culture that emphasizes advancement and performance-based pay. The goal is to move shipments reliably and efficiently while expanding market share and providing employees with clear paths to career growth and earnings based on performance.

Company size
5,001-10,000
Company stage
Not listed
Total funding
Not listed
Headquarters
Cincinnati, OH
Founded
1997

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