AI Architect

General information

Country

USA

State

Ohio

City

Cincinnati

Descriptions & requirements

About the role:

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.

What’s in it for you:

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 Certified Great Place to Work and voted a  Computerworld Best Places to Work in ITWhat you’ll be doing:

AI Strategy & Enterprise Architecture 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

Application Architecture & Integration 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

Solution Design & Technical Leadership 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 Data & Integration Architecture 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 strategiesMLOps, Governance & Operational Readiness 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

Innovation & Continuous Improvement 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…