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Bhuvan Chandi
Mid-level Data Engineer specializing in AI/ML data platforms
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Built and productionized an LLM-powered PDF document Q&A system to eliminate manual searching through long documents, focusing on scalability and answer reliability. Implemented semantic chunking (using headings/paragraphs/tables), overlap, and preprocessing/quality checks to reduce hallucinations, and orchestrated the end-to-end pipeline with Airflow using retries, alerts, and parallel tasks.
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Key Strengths
- Built and deployed production LLM document Q&A system for PDF knowledge access
- Improved answer accuracy by semantic chunking (headings/paragraphs/tables) with overlap and preprocessing filters
- Designed scalable async pipelines to handle high user volume
- Used Airflow to orchestrate multi-step pipelines with retries/alerts and parallelization to reduce failures and processing time
- Metrics-driven evaluation of AI workflows (accuracy, retrieval quality, latency, failure rates) with monitoring and fallback logic
- Pragmatic model selection via benchmarking for accuracy/speed/cost tradeoffs
- Effective collaboration with non-technical stakeholders via plain-language problem framing and iterative prototypes
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“Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.”
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