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Zohaib Shahid
Mid-level Data Scientist specializing in Generative AI and LLM solutions
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Built and owned a production RAG-based internal knowledge assistant end-to-end, from experimentation through cloud deployment and monitoring. Demonstrated strong practical GenAI judgment by choosing prompt optimization and retrieval tuning over fine-tuning for dynamic data, driving a 40% to 50% reduction in time to answer while improving relevance, lowering hallucinations, and increasing productivity.
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Key Strengths
- Built production RAG knowledge assistant end-to-end
- Improved support and knowledge resolution time by 40% to 50%
- Strong retrieval optimization using chunking, query rewriting, and metadata filtering
- Pragmatic GenAI architecture choices favoring RAG over fine-tuning for dynamic data
- Production monitoring across latency, cost, retrieval quality, and user feedback
- Reduced hallucinations through grounded prompt and retrieval design
- Scaled Python services with modular APIs and containerized CI/CD
- Improved team reuse and deployment velocity through standardized RAG service components
- Applied caching to reduce LLM calls and lower latency
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