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Misam
Abbas
Staff AI Engineer
LinkedIn
Misam Abbas is a Staff AI Engineer specializing in Search and Recommendation systems and Responsible AI. Misam has varied experience at leading technology companies including LinkedIn, Dropbox, and Meta, he has made significant contributions to fairness metrics for recommender systems and built large-scale AI systems serving billions of users. At Meta, he developed sophisticated user representation models adopted across multiple product teams. At Dropbox, he led the development of their universal search capability combining semantic and lexical approaches. Currently at LinkedIn, he focuses on responsible AI frameworks for both traditional machine learning and LLM-based systems. Misam holds an MBA from London Business School and a B.Tech in Computer Science from the Indian Institute of Technology. His work focuses on building ethical AI systems and developing frameworks for measuring and improving algorithmic fairness at scale.
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29 May 2025 09:45 - 10:30
Tackling hallucinations, drift & data decay at scale
LLMs don’t fail overnight, they degrade quietly. From prompt fragility to distributional drift, maintaining reliability in production demands more than one-time testing. This session explores how engineering teams are: → Detecting and mitigating drift across prompts, embeddings, and outputs → Designing feedback loops that surface hallucinations in real-world use You'll leave with proven strategies to reduce failure rates, preserve model accuracy, and ensure your LLMs stay robust as data, users, and use cases evolve.

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