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Ralish Routray
Mid-level Data Scientist & Machine Learning Engineer specializing in fraud and forecasting
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ML/LLM practitioner who has shipped production RAG systems (summarization + Q&A) and end-to-end Airflow-orchestrated demand forecasting pipelines at NEON IT. Strong focus on reliability—uses evaluation scripts, retrieval/chunking tuning, validation/retries/alerts, and stakeholder-driven iteration to make AI workflows consistent and usable.
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Key Strengths
- Built and deployed an LLM-based document summarization + Q&A system via Flask API
- Improved RAG reliability by tuning chunking/retrieval filters and adding evaluation against human-written samples
- Designed reliable Airflow-orchestrated ML pipelines (ingest → features → train → publish forecasts to internal API)
- Hardened pipelines against late/missing data using validation checks, retries, and alerts
- Pragmatic model/prompt selection: tests smaller models first; identifies retrieval vs model as root cause for quality issues
- Effective collaboration with non-technical supply chain stakeholders to translate decisions into model inputs/outputs
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