AI Architect

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 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 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 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 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 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 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 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. #J-18808-Ljbffr Total Quality Logistics