Associate Solution Designer

Key Responsibilities: Design and implement AI based solutions to solve business challenges in Managed Transportation, including optimization, automation, forecasting, and anomaly detection. Build and deploy intelligent systems that enhance real time decision making and oversight across operational workflows. Collaborate with cross functional teams to identify high impact use cases where AI can drive measurable outcomes. Develop and integrate AI agents/copilots to assist Sales and Operations teams with insights, recommendations, and customer support automation. Work with large scale transactional and operational data typical in transportation systems, ensuring high data quality and effective data pipelines. Contribute to the development and operationalization of AI/ML models, ensuring performance, scalability, and maintainability. Lead or support MLOps practices, including version control, continuous integration, deployment pipelines, and model monitoring. Required Experience Qualifications: Proven experience in the design, development, and deployment of AI/ML solutions in enterprise or logistics focused environments. Strong understanding of machine learning lifecycle management, including data preparation, model development, validation, deployment, and monitoring. Experience working with large, structured and unstructured datasets, particularly from operational and transactional systems such as TMS, ERP, or telematics platforms. Familiarity with cloud based AI platforms and modern data engineering frameworks. Knowledge of foundational technologies such as messaging systems, distributed storage, real time data processing, and scalable compute environments. Exposure to or experience with LLMs (Large Language Models) and SLMs (Small Language Models), including their application in enterprise contexts (e.g., intelligent assistants, summarization, document classification, etc.). Experience implementing or supporting AI agent frameworks or conversational systems to assist end users in business workflows. Understanding of MLOps best practices, including automation, monitoring, and governance of AI models in production. Preferred Qualifications: Background in transportation, supply chain, or logistics domains, particularly in a Managed Transportation Services (MTS) setting. Ability to work with cross functional stakeholders across Operations, Sales, Product, and Data teams. Experience in developing AI use cases like: Predictive ETAs Load consolidation optimization Delay/risk identification Carrier performance analysis Cost or carbon footprint forecasting Soft Skills: Strong problem solving skills and analytical thinking. Excellent communication and collaboration abilities. Ability to translate technical solutions into business value. Comfortable working in a fast paced, evolving environment.