ML/AI Engineer with 2+ years building production LLM systems, deep learning models, and scalable data pipelines delivering $229K+ verified business impact across robotics, marketing intelligence, and e-commerce. Proficient in Python, PyTorch, and TensorFlow with hands-on experience in GenAI, RAG, end-to-end MLOps, and distributed data engineering.
I came to ML through Electronics Engineering. Hardware teaches you that latency compounds, systems degrade under load, and the gap between a prototype and something production-ready is almost never just a code problem. That mindset is in everything I build.
Currently at Capital One: Real-time fraud detection across 12M daily card transactions, $8.4M annual fraud-loss reduction, 31% fewer false-positive declines, and production ML serving with Kafka, Spark, XGBoost, and EKS.
"Always in Beta. Always Compounding."
I'm actively looking for my next role. If you're working on something hard and care about what gets shipped, let's talk.
Roles I'm targeting:
Industries: Worked across FinTech, Robotics, Marketing and AI. Always looking for new domains to dive in
| Domain | Tools |
|---|---|
| Languages | Python · C# (.NET) · C++ · R · Go · SQL (PostgreSQL · MySQL) · React · TypeScript · Git |
| GenAI / NLP | LangChain · LangGraph · LlamaIndex · RAG · Whisper ASR · BERT · Transformers · LoRA · QLoRA · PEFT · spaCy · NLTK · Vector DBs · FAISS · ChromaDB |
| ML / DL | PyTorch · TensorFlow · TFLite · Keras · XGBoost · LightGBM · SHAP · CUDA · CNN · LSTM · RNN · GANs · BLIP-2 · statsmodels · SciPy · Hugging Face |
| Computer Vision | YOLOv8 · ByteTrack · MediaPipe · ResNet · EfficientNet · Tesseract OCR · OpenCV |
| MLOps / Cloud | Docker · Kubernetes · MLflow · Airflow · AWS SageMaker · AWS Lambda · Azure · GCP · Terraform · FastAPI · Kafka · CI/CD |
| Data Engineering | Spark · PySpark · dbt · Snowflake · ETL · Redis · MySQL · PostgreSQL · MongoDB |
| Analytics / BI | Tableau · Power BI · Looker · Plotly · Streamlit · A/B Testing · Causal Inference · Hypothesis Testing · Bayesian Methods · Propensity Score Matching |
- 🔍 FinSight RAG - Hybrid RAG pipeline with MiniLM embeddings, dense/sparse retrieval, and semantic reranking over SEC 10-K filings. Benchmarks 7 retrieval strategies via an LLM-as-judge framework. 94% query success · 4.25/5 relevance · 42% latency cut · 40% API cost reduction
- 🎵 Speech Emotion Recognition - CNN-LSTM with MFCC, mel-spectrogram, and chroma feature extraction on 15K+ audio samples. 90.5% accuracy · 90.4 F1 across 8 classes · outperformed InceptionV3 baseline by 3% while training 25% faster
- 🦅 Bird Species Classification - 4 CNN architectures benchmarked on 89,885 images across 100 species. Deep VGG-style CNN vs. InceptionV3 transfer learning. 90.5% accuracy · 90.4 F1
- 🧬 NeuroDigest AI - Agentic LLM pipeline using LangChain reasoning chains to ingest multi-format sources and generate structured digests
I'm always open to conversations about interesting problems and the right opportunities. Reach out through any of the channels below.
