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Jarin Tasnim
Staff/Lead Software Engineer specializing in distributed data and ML platforms
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About
Defense-domain AI engineer who built a production ReAct-style RAG system for military training data/material generation, scaling to ~1000 users and cutting generation time by 50%. Also has experience designing GPU-cluster parallel computation with PyTorch and handling production incidents involving database performance and schema design.
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Key Strengths
- Built and deployed ReAct-based RAG system from prototype (Gradio) to production
- Scaled LLM system to support ~1000 concurrent training users
- Reduced training material generation time by 50%
- Designed and validated GPU-cluster parallel computation via benchmarking and GPU monitoring
- Strong incident debugging: traced release-blocking slowdown to inefficient DB table updates and documented improved design
- Effective ambiguity ownership: defined baseline requirements via domain-expert outreach, shipped prototype, and iterated via prioritized feedback
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Senior AI Engineer specializing in LLMs, RAG, and production ML systems
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