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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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
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