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 packageHealth, 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 ArchitectureEvaluate 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 & IntegrationDesign 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 ArchitecturePartner 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 ImprovementEvaluate 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.
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 packageHealth, 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 ArchitectureEvaluate 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 & IntegrationDesign 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 ArchitecturePartner 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 ImprovementEvaluate 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.