I’m a Data Scientist with hands-on experience building and deploying real-world machine learning, predictive analytics, and Generative AI systems in production environments. I have worked on end-to-end solutions including RAG-based knowledge automation, brand-aligned LLM translation pipelines, time-series forecasting, anomaly detection, and equipment failure prediction. My work spans the full lifecycle from data ingestion using Kafka and MQTT, to model development in Python, to API serving with FastAPI and deployment using Docker and Kubernetes. I primarily work with Python, SQL, machine learning, GenAI, Elasticsearch, InfluxDB, Postgres, and cloud-native architectures, with a strong focus on scalability, accuracy, and measurable business impact. I enjoy breaking down complex concepts, explaining the reasoning behind design choices, and helping others build practical, interview-ready skills based on real industry use cases.