Machine Learning & MLOps — Building end-to-end pipelines: ingestion → training → tracking → deployment
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Solo-built pipeline streaming live BTC/USDT trades from the Binance WebSocket API through Kafka into Spark Structured Streaming, landing raw and 1-min OHLC data as Parquet in a MinIO data lake, served via Spark Thrift Server to live Superset dashboards. Diagnosed and fixed a race condition that could corrupt Structured Streaming's exactly-once metadata log.
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End-to-end system generating, ingesting, and analyzing application logs in real time. A FastAPI + PostgreSQL ingestion layer feeds an Isolation Forest model that flags anomalies — traffic spikes, server errors, suspicious IPs — on a live Streamlit dashboard.
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End-to-end pipeline ingesting live data from Yahoo Finance, Reuters, Reddit, and Twitter to predict market direction and volatility using RNN, LSTM, and GRU models. Full MLOps stack with a FastAPI REST API deployed on AWS EC2.
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Designed and benchmarked three architectures — Seq2Seq BiLSTM, BART, and a custom T5+Hybrid Attention — on CNN/DailyMail. Custom model reached 91% of BART's performance with 45% fewer parameters (62M vs 139M).
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Multi-class facial recognition system classifying 16 public figures on 1,280+ images. Fine-tuned ResNet-50 and EfficientNet to 90%+ accuracy, versioned with DVC, tracked via MLflow, deployed on AWS EC2.
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Open to entry-level roles in AI/ML Engineering · Data Engineering · Data Science