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:
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Competitive compensation
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Opportunity to influence enterprise‑wide AI architecture
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High visibility partnership with executive leadership
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Long‑term career growth in a collaborative, AI‑driven organization
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Comprehensive benefits package
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Health, dental and vision coverage
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401(k) with company match
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Perks including employee discounts, financial wellness planning, tuition reimbursement and more
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Certified Great Place to Work and voted a 2019-2026 Computerworld Best Places to Work in IT
What we’re looking for:
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Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, AI/ML or a related field
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Azure certifications (Solutions Architect, Azure AI Engineer) preferred
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7–12+ years of experience in AI/ML engineering, cloud architecture or enterprise software engineering
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Proven experience architecting and delivering production AI or ML solutions on Azure
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Experience with REST APIs, serverless functions, microservices and event-driven architectures
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Backend development in Python with working knowledge of C# or Node.js.
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Hands-on experience with Azure OpenAI, Azure Machine Learning, Azure AI Search, Microsoft Fabric and Lakehouse architectures
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Experience with embeddings, vector databases, RAG patterns, LangChain, Semantic Kernel and MLflow
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Proficiency with Git, Azure DevOps CI/CD, Docker and Kubernetes
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Strong understanding of data modeling, governance, lineage and security
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Strong communication skills across technical and non-technical audiences
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Ability to translate business workflows into scalable technical architectures
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Strong ownership mindset with focus on reliability, cost optimization and long-term scalability
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Product mindset with ability to align AI architecture to business outcomes
What you’ll be doing:
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AI Strategy & Enterprise Architecture
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Evaluate and recommend AI models, APIs and platforms (e.g., Anthropic, OpenAI, Microsoft, Google) based on security, reliability, cost and enterprise fit
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Define the enterprise AI architecture across Azure OpenAI, Azure AI Search, Microsoft Fabric, Azure ML, APIs, event-driven systems and operator-facing tools
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Establish standards for building LLM applications, retrieval-augmented generation (RAG) systems, intelligent agents and ML models at scale
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Create reference architectures for AI-powered solutions including real-time workflows, automation, copilots and knowledge assistants
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Application Architecture & Integration
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Design how AI services integrate with core applications, including broker tools, APIs, workflows and backend services
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Establish patterns for serverless functions, microservices, REST APIs, event-driven pipelines and end-to-end orchestration
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Partner with application development teams to embed AI into product features with the right performance, security, authentication and data flow patterns
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Ensure AI solutions meet enterprise CI/CD, observability, reliability and SLA standards
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Solution Design & Technical Leadership
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Lead solution designs for AI platforms including vector databases, embedding pipelines, inference services, feature stores and model registries
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Translate complex operator workflows into scalable, AI-enabled architectures that improve decision-making and productivity
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Conduct architecture, design reviews and mentor AI Engineers, Software Engineers, Data Engineers and Data Scientists
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Data & Integration Architecture
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Partner with Data Engineering to ensure Fabric Lakehouse, Delta tables, warehouse layers and streaming systems support both training and inference workloads
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Architect and optimize RAG pipelines using Azure AI Search, vector indexing, embeddings and metadata strategies
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MLOps, Governance & Operational Readiness
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Define and implement enterprise MLOps standards for model lifecycle management, versioning, monitoring and retraining
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Apply Responsible AI practices including content filtering, privacy, compliance and hallucination mitigation
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Ensure AI systems are observable with performance and cost monitoring
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Innovation & Continuous Improvement
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Evaluate emerging AI models, agent frameworks and Azure capabilities for use in logistics workflows
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Lead proofs of concept (PoCs) and accelerate adoption of high-value AI initiatives
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Develop reusable technical playbooks and architectural patterns to mature AI across engineering teams
Where you’ll be: 200 Regency Executive Park Drive, Charlotte, North Carolina 28217
Employment visa sponsorship is unavailable for this position. Applicants requiring employment visa sponsorship now or in the future (e.g., F-1 STEM OPT, H-1B, TN, J1 etc.) will not be considered.
