Pradeep Kumar

Pradeep Kumar

Senior Software Engineer

Emirates

Pradeep Kumar is a Dubai-based AI/ML Engineer and Software Leader with over 12 years of experience delivering scalable, intelligent systems across industries. Currently a Senior Software Engineer at Emirates Airlines, he architects agentic AI solutions that combine vision models and large language models (LLMs) to automate operational intelligence in aviation, one of the most regulated industries in the world. He holds a Master’s in Artificial Intelligence and Machine Learning from Liverpool John Moores University, UK, and has a strong track record of translating complex AI concepts into real-world applications that drive efficiency, safety, and innovation. A regular guest lecturer and speaker, Pradeep brings a unique blend of hands-on expertise and thought leadership to the evolving conversation around multi-modal, agentic, and responsible AI.

Modern AI systems are evolving to see, reason and act. In this session, we explore designing an agentic AI system that combines computer vision with large language models (LLMs) to detect uniforms and trigger intelligent, context-aware events like granting access, sending alerts, or logging events. The system architecture includes prompt chaining, lightweight APIs, and agent frameworks, along with safeguards like confidence thresholds and human-in-the-loop logic. Attendees will gain insights into how such systems can be applied across aviation, logistics, retail, and security integrating perception, reasoning, and response for scalable, responsible automation. The session closes with a hands-on demo using synthetic visual inputs and real-time LLM-based decision-making.

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Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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