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Sravani Gangaraju
Mid-level AI/ML Engineer specializing in NLP, computer vision, and recommender systems
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About
Built and deployed a production NLP sentiment analysis system at Piper Sandler to turn noisy, finance-specific customer feedback into scalable insights. Demonstrates strong end-to-end MLOps: fine-tuning BERT, improving label quality, monitoring for language drift, and automating retraining/deployment with Airflow and Docker (plus Kubeflow exposure).
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Key Strengths
- Built and deployed production NLP sentiment analysis system to scale manual feedback analysis
- Improved model performance by customizing preprocessing for noisy, domain-specific finance text
- Prioritized label quality improvements with stakeholders for higher impact than model swapping
- Fine-tuned BERT-based model to better capture context vs simpler approaches
- Implemented drift monitoring (confidence + sentiment distribution) with retraining triggers
- Automated retraining/deployment via Airflow to replace manual processes
- Resolved pipeline environment mismatches by containerizing tasks with Docker
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