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Aditi Deshpande
Mid-level Software/AI Engineer specializing in GenAI, AWS, and microservices
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About
Built a production AI pipeline at EyCrowd to automatically grade shaky outdoor user-submitted brand videos using CV + CLIP/BLIP and a LangChain RAG layer per brand, with GPT-4 generating structured JSON explanations and grades. Optimized for latency and cost (batch PyTorch inference, caching), cutting review time from ~8 minutes to <2 minutes while reaching ~90% alignment with human graders and supporting thousands of videos/day.
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Key Strengths
- Built and deployed end-to-end automated video evaluation pipeline to production
- Reduced review time from 8–10 minutes per video to under 2 minutes end-to-end
- Scaled system to handle thousands of videos per day
- Achieved ~90% agreement with human graders (validated on 200+ videos)
- Cost-optimized LLM/CV pipeline (~$0.15–$0.25 per video; ~$150/day for 1,000 videos)
- Designed modular agent/workflow architecture with deterministic steps and checkpoints (perception, retrieval, reasoning, output)
- Reliability techniques: strict JSON validation, fallback LLM, caching, regression testing on prompt/model/retrieval changes
- Strong cross-functional collaboration translating non-technical review criteria into measurable CV/LLM signals
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