Designing production-grade ML systems, scalable data pipelines, and applied AI solutions — from raw data to deployed models.
const abhinav = {
role: "Senior Data Scientist & AI/ML Engineer",
focus: ["Machine Learning", "Deep Learning", "Applied AI / GenAI", "MLOps"],
currently: "Building scalable, production-grade ML systems & data pipelines",
lifecycle: "framing → data pipelines → modeling → deployment → monitoring",
toolbelt: "from GPU training clusters down to nginx configs & SQL warehouses",
askMeAbout: ["RAG & Knowledge Graphs", "Agentic AI", "Forecasting", "Computer Vision"],
funFact: "A well-tuned data pipeline is as satisfying as a clean model fit 📈"
};
🧠 Machine Learning & AI
🤖 Applied AI & GenAI
|
👁️ Computer Vision
☁️ Data Engineering & MLOps
|
| Project | What it does | Stack |
|---|---|---|
| 🤖 RAG Knowledge Assistant | Retrieval-augmented Q&A over custom documents using embeddings + LLMs. | Python · LangChain · Qdrant |
| 📈 Time-Series Forecasting | End-to-end forecasting with feature engineering, backtesting & model comparison. | Python · scikit-learn · XGBoost |
| 👁️ Real-Time Object Detection | Detection & tracking pipeline with a lightweight inference API. | PyTorch · OpenCV · FastAPI |
| 🚀 ML Model Serving API | Containerized inference service with experiment tracking & monitoring. | FastAPI · Docker · MLflow |
Always learning, always shipping. ⚡