AI Engineer (Agentic AI Systems)

About the Role
LOGFLOWS is an AI-powered logistics platform (TMS, LMS, and Docken dock management) used by logistics operators to run their day-to-day operations. We're building the agentic AI layer on top of the platform that automates real logistics workflows from document processing, warehouse operations, RFQ/RFP handling, to  intelligent retrieval over shipment and order data. We're looking for a hands-on AI Engineer on the various AI model stack to help architect and scale these agentic systems from prototype into production, working directly with our core TMS/LMS data and APIs.

Key Responsibilities

• Design and build autonomous AI agent workflows for logistics use cases - including document OCR (e.g. delivery notes, invoices), warehouse workflow automation, and RFQ/RFP automation - featuring multi-step reasoning, dynamic tool-calling, memory management, and orchestration.
• Architect and fine-tune RAG pipelines over TMS/LMS order and shipment data, using Azure AI Search, vector databases, and custom retrieval/ranking logic.
• Build modular, maintainable AI applications using orchestration frameworks such as LangChain, Semantic Kernel, or LlamaIndex, integrated with Logistics software existing product APIs.
• Integrate NLP and computer vision models (e.g. document/QR/label recognition) into the broader web and mobile ecosystem.
• Package, deploy, and maintain containerized AI workloads on Kubernetes or OpenShift, ensuring enterprise-grade scalability and uptime.
• Conduct production monitoring, A/B testing, latency optimization, and system performance tuning for agentic workflows running against live logistics data.
• Explore and prototype emerging interoperability standards (e.g., Model Context Protocol / MCP) to extend agent capabilities across the company’s product suite.
• Collaborate with product / Software engineering, and business development teams to translate real customer logistics workflows into agentic AI features; conduct code reviews and produce clean technical documentation.

Requirements & Qualifications

• Degree in Computer Science, Software Engineering, Data Science, or a related technical discipline.
• Hands-on experience engineering and deploying production-grade AI systems or LLM applications (proven track record over years of study alone).
• Strong practical experience with the Azure AI Stack (Azure OpenAI Service, Azure AI Search, Azure Machine Learning).
• Hands-on experience with LLM orchestration frameworks (LangChain, LlamaIndex, or Semantic Kernel).
• Proficient in Python for AI/ML development and system integration.
• Practical experience with containerization and orchestration tools (Docker, Kubernetes, or OpenShift).
• Solid understanding of agentic architectures, function calling, state management, and enterprise system design.
• Interest in or exposure to logistics, supply chain, or transportation operations is a plus, but not required - we'll teach the domain.
• Strong problem-solving abilities and communication skills, comfortable working in a fast-moving product team.