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Bhavya Sri Gunnapaneni
Mid-level AI/ML Engineer specializing in fraud detection and NLP
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About
Built production AI/RAG-style systems for message Q&A and insurance claims workflows, combining data ingestion, indexing/retrieval, and LLM integration with fallback modes. Has hands-on orchestration experience (Airflow, Prefect, LangChain) and cites large operational gains (claims processing reduced to ~45 seconds; manual review -50%; false alerts -30%) through automated, monitored pipelines and close collaboration with non-technical stakeholders.
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Key Strengths
- Built and deployed an AI system to answer questions over member messages using external API ingestion + indexing + retrieval
- Improved messy/inconsistent text data quality via preprocessing (cleaning, timestamp fixes, deduplication)
- Designed for scalability by planning retrieval swap from TF-IDF to embeddings + vector store
- Implemented graceful fallback behavior when OpenAI API key/environment not available
- Orchestrated multi-step fraud/claims pipelines with Airflow; reduced processing time from hours to ~45 seconds
- Reliability-focused workflow design: modular components, dry-run simulations, edge-case testing, post-deploy monitoring
- Stakeholder collaboration: translated non-technical insurance team needs into an AI-driven workflow with demos and dashboards
- Reported impact in claims automation: cut manual review time by 50% and reduced false alerts by 30%
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