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Tejaswi Rao
Mid-level Machine Learning Engineer specializing in MLOps and GenAI analytics
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ML/LLM practitioner who has deployed a production RAG-based trouble-call identifier using multiple datasets (device, network, past complaints). Experienced in end-to-end MLOps (FastAPI + Docker + Kubernetes with HPA) and in evaluating/monitoring LLM behavior to reduce hallucinations, with additional applied work in forecasting/anomaly detection and churn prediction for retention campaigns.
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Key Strengths
- Built and deployed a production RAG system for trouble-call root cause identification and solutioning
- Performance optimization in production (latency reduction via regional server placement and Kubernetes HPA autoscaling)
- LLM quality evaluation and model selection using hallucination/LLM metrics (selected Mistral 7B based on results)
- End-to-end ML deployment workflow (Python/EDA → FastAPI inference service → Docker → Kubernetes)
- Communicating model outputs to non-technical stakeholders via BI dashboards (Power BI, Tableau)
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