ML / Data Engineer — building RAG systems, data pipelines, and poking at what's inside LLMs
MS CS @ UT Dallas (May 2026) · Dallas, TX
- 🔍 Digging into mechanistic interpretability — trained an SAE on GPT-2-small and built a live safety monitor from it
- 🛠️ Shipped a RAG evaluation tool at an OpenAI hackathon (Build Week)
- 💼 Past experience across ML (IoT/CV at MIT-WPU × Capgemini), data engineering (DRDO — LiDAR/camera perception pipelines), and TA'ing DSA at UT Dallas
- 🎓 MS in Computer Science from UT Dallas, open to full-time ML / Data Engineering roles
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ActivationLens Mechanistic interpretability on GPT-2-small — trained a sparse autoencoder on the layer-6 residual stream, cut dead-feature collapse from ~80% to 0.77%, then built a live per-token safety monitor (0.758 AUROC) with a one-pass kernel that trimmed monitoring overhead 6x.
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OpenAI Build Week GPT-5.6-powered tool that scores, diagnoses, and auto-tunes RAG pipeline outputs — built with Codex during OpenAI's Build Week hackathon.
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DocumentSync AI RAG pipeline over 30 ArXiv papers across 4 chunking strategies, evaluated with RAGAS across 120 LLM-as-judge calls — best config improved context precision by 59% and recall 3x over baseline.
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Real-Time Crypto Streaming Pipeline Streams live market data via Kafka + Spark Structured Streaming into a Redshift star schema, orchestrated with Airflow — cut Athena bytes scanned by 89%.
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