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Final-year CS undergraduate specializing in AI/ML, building high-performance systems at the intersection of machine learning, GenAI engineering, and production infrastructure.
- 🔭 Currently building end-to-end GenAI systems — RAG pipelines, multi-agent orchestration (LangGraph), and evaluation harnesses, built from first principles rather than framework shortcuts
- 💼 Dual concurrent internship experience at MEDxAI and Waisl Technologies
- 🧠 Strong foundation in classical ML, NLP (HuggingFace transformers), and cloud-native ML infrastructure (GCP, Apache Beam, Terraform)
- 🌱 Currently deep-diving into hybrid retrieval, reciprocal rank fusion, and agent eval/observability design
- 🎯 Looking to collaborate on ML/GenAI projects with real production constraints — deployment, monitoring, and cost/latency tradeoffs, not just notebooks
- 📫 Reach me at [email protected]
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Financial forecasting system combining FinBERT sentiment inference with XGBoost/LightGBM ensembling, walk-forward validation, SHAP explainability, and full backtesting.
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Full-stack data quality monitoring platform: React 19 + MUI v9 frontend, FastAPI backend, deployed on AWS (Lambda, S3, DynamoDB, SNS) — flags bad data via automated alerting pipelines.
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NLP research project on multilingual aspect-based sentiment analysis using contrastive learning — a core differentiator in the portfolio's research depth.
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On-device audio processing achieving 20–25 dB SDR improvement with real-time inference at ~370ms latency, optimized for edge deployment.
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End-to-end RAG + multi-agent system — semantic chunking, hybrid retrieval (BM25 + dense + RRF), Planner/Retriever/Critic/Writer agents, and a full evaluation harness for faithfulness and regression tracking.
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Languages
ML / AI / GenAI
Data & Infra
Backend & Web
Tools
