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Dhruv Pandoh
Junior Full-Stack Software Engineer specializing in AI, FinTech, and e-commerce
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About
Built both traditional internal tooling and LLM-powered systems during an internship, including a React/Python/AWS calculator onboarding platform and a production-style ROS2 RAG assistant over 10K+ documents. Stands out for combining full-stack delivery, stakeholder coordination, and practical AI reliability work like retrieval tuning, source-grounded answers, and low-confidence fallbacks.
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Key Strengths
- owned end-to-end internal platform deployment from discovery through post-launch stabilization
- strong stakeholder management across quant users and engineering teams
- built reliable frontend-backend workflows with layered validation
- hands-on RAG system design and productionization
- grounding-first approach to LLM accuracy using retrieval and source attribution
- systematic debugging of non-deterministic AI pipeline issues
- improved trust through fallback logic for insufficient context
- translated messy Excel-based processes into structured API-driven workflows
- Shipped production RAG assistant for ROS2 developer support across docs/GitHub/YouTube sources
- Designed reliability-focused LLM system with fallbacks and graceful degradation (retrieval/embeddings/keyword fallback)
- Built deterministic multi-step agent pipeline with explicit orchestration and structured outputs (answer/sources/confidence)
- Implemented monitoring and observability (step-level logging, latency/failure tracking) using ClearML
- Established offline + online evaluation loop; added low-confidence retrieval guardrail to reduce hallucinations
- Experience handling messy multi-source data with preprocessing, normalization, deduplication, and idempotent ingestion
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