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Total Quality Logistics

Freight brokerage linking shippers to carriers

AI Architect

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeCincinnati, OH, USANo salary listedIn-person

About the Job

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, AI/ML or a related field.
  • Seven to twelve or more years of experience in AI/ML engineering, cloud architecture or enterprise software engineering.
  • Proven experience architecting and delivering production AI or machine learning solutions on Azure.
  • Experience with REST APIs, serverless functions, microservices and event-driven architectures.
  • Backend development experience 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, retrieval-augmented generation patterns, LangChain, Semantic Kernel and MLflow.
  • Proficiency with Git, Azure DevOps continuous integration and continuous delivery, 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 focused on reliability, cost optimization and long-term scalability.
  • Product mindset and ability to align AI architecture to business outcomes.

Responsibilities

  • Define and lead the enterprise-wide AI architecture that powers intelligence and automation across the logistics and freight brokerage ecosystem, ensuring solutions are secure, reliable, cost-efficient and integrated into business workflows.
  • Partner with Engineering, Product Management, Data and Operations leadership to identify high-impact use cases and deliver AI capabilities across 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 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 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 complex 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 machine learning operations standards for model lifecycle management, versioning, monitoring and retraining.
  • Apply responsible AI practices, including content filtering, privacy, compliance and hallucination mitigation.
  • Ensure AI systems have observability for 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.

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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