General informationCountry
USA
State
North Carolina
City
Charlotte
Descriptions & requirementsAbout 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
What we're looking for: - Bachelor's or Master's degree in Computer Science, Data Science, Engineering, AI/ML or a related field
- Azure certifications (Solutions Architect, Azure AI Engineer) preferred
- 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
What 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 strategies
- MLOps, 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 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.
