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Yashi Agarwal
Mid-level Machine Learning Engineer specializing in NLP, Generative AI, and RAG systems
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About
Built and deployed a production LLM-powered phone assistant for a healthcare clinic, combining streaming STT/TTS with RAG over approved clinic documents and strict safety guardrails to prevent unverified medical advice, plus seamless human handoff. Also has hands-on Apache Airflow experience building robust daily ML/data pipelines with data validation, retries/timeouts, monitoring, and metric-gated model deployment, and iterates closely with clinic staff using real call reviews.
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Key Strengths
- Built and shipped a production LLM-powered phone assistant for a healthcare clinic with human handoff
- Strong safety/compliance approach for healthcare use cases (RAG over approved docs + strict prompting + code-level checks)
- Latency-focused engineering for real-time voice loops (streaming STT, smaller model for simple queries, prompt shortening)
- Reliability engineering in production (timeouts, retries, clear fallbacks, robust logging/alerts)
- Practical evaluation discipline (unit tests, scripted end-to-end flows, reusable prompt eval set, ongoing session reviews)
- Airflow expertise building resilient ML/data pipelines with data checks, retries/timeouts, and metric-based model promotion
- Effective collaboration with non-technical stakeholders via iterative pilots and weekly call reviews
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