AI Architect

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 compensationOpportunity to influence enterprise‑wide AI architectureHigh visibility partnership with executive leadershipLong‑term career growth in a collaborative, AI‑driven organizationComprehensive benefits packageHealth, dental and vision coverage401(k) with company matchPerks including employee discounts, financial wellness planning, tuition reimbursement and moreCertified Great Place to Work and voted a 2019-2026 Computerworld Best Places to Work in IT

What we’re looking for:

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, AI/ML or a related fieldAzure certifications (Solutions Architect, Azure AI Engineer) preferred7–12+ years of experience in AI/ML engineering, cloud architecture or enterprise software engineeringProven experience architecting and delivering production AI or ML solutions on AzureExperience with REST APIs, serverless functions, microservices and event-driven architecturesBackend 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 architecturesExperience with embeddings, vector databases, RAG patterns, LangChain, Semantic Kernel and MLflowProficiency with Git, Azure DevOps CI/CD, Docker and KubernetesStrong understanding of data modeling, governance, lineage and securityStrong communication skills across technical and non-technical audiencesAbility to translate business workflows into scalable technical architecturesStrong ownership mindset with focus on reliability, cost optimization and long-term scalabilityProduct mindset with ability to align AI architecture to business outcomes

What you’ll be doing:

AI Strategy & Enterprise ArchitectureEvaluate and recommend AI models, APIs and platforms (e.g., Anthropic, OpenAI, Microsoft, Google) based on security, reliability, cost and enterprise fitDefine the enterprise AI architecture across Azure OpenAI, Azure AI Search, Microsoft Fabric, Azure ML, APIs, event-driven systems and operator-facing toolsEstablish standards for building LLM applications, retrieval-augmented generation (RAG) systems, intelligent agents and ML models at scaleCreate reference architectures for AI-powered solutions including real-time workflows, automation, copilots and knowledge assistantsApplication Architecture & IntegrationDesign how AI services integrate with core applications, including broker tools, APIs, workflows and backend servicesEstablish patterns for serverless functions, microservices, REST APIs, event-driven pipelines and end-to-end orchestrationPartner with application development teams to embed AI into product features with the right performance, security, authentication and data flow patternsEnsure AI solutions meet enterprise CI/CD, observability, reliability and SLA standardsSolution Design & Technical LeadershipLead solution designs for AI platforms including vector databases, embedding pipelines, inference services, feature stores and model registriesTranslate complex operator workflows into scalable, AI-enabled architectures that improve decision-making and productivityConduct architecture, design reviews and mentor AI Engineers, Software Engineers, Data Engineers and Data ScientistsData & Integration ArchitecturePartner with Data Engineering to ensure Fabric Lakehouse, Delta tables, warehouse layers and streaming systems support both training and inference workloadsArchitect and optimize RAG pipelines using Azure AI Search, vector indexing, embeddings and metadata strategiesMLOps, Governance & Operational ReadinessDefine and implement enterprise MLOps standards for model lifecycle management, versioning, monitoring and retrainingApply Responsible AI practices including content filtering, privacy, compliance and hallucination mitigationEnsure AI systems are observable with performance and cost monitoringInnovation & Continuous ImprovementEvaluate emerging AI models, agent frameworks and Azure capabilities for use in logistics workflowsLead proofs of concept (PoCs) and accelerate adoption of high-value AI initiativesDevelop reusable technical playbooks and architectural patterns to mature AI across engineering teams

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.