pruning , Machine Learning Algorithms, Image Processing, Tensorflow, Pytorch, Docker, Ml, Computer Vision, Deep Learning, Neural Networks, Opencv, Python Programming, Data Storage, Model optimization techniques, Hyperparameter optimization, TensorRT, Quantization, Feature engineering, Dataset preparation, RCNN, Knowledge distillation, Linux based servers, ML Flow, Deep learning architectures, Cloud platforms, Scikit-learn, Ai, Model training, RTDETR, YOLO

testing practices , Nosql, Java, React, Python, AWS, LangChain, pgvector, asynchronous processing, observability

alerting), Observability (logs, Retrieval-enabled architectures (RAG), Java, React, metrics, Python, LLM prompt capabilities, Event-driven and asynchronous processing patterns, tracing, Cloud-native development, Vector search embeddings, Governance expectations, CI/CD pipelines, SRE-style operational practices, Secure SDLC practices, LangChain or similar frameworks, Release automation

Retrieval-Augmented Generation (RAG) solutions, Cursor, Code, embeddings, vector search, data pipelines, vector databases, data infrastructure, AI-assisted software development tools, Document Processing, AI-powered software development tools, LLM-based applications, data quality monitoring, GitHub Copilot, Evaluation, model data pipelines, structured and unstructured data, Claude, observability, deployment workflows

Algorithms, Java, Tensorflow, Gcp, Pytorch, data structures, Azure, Python, AWS, scikit-learn, data preprocessing, R, model evaluation, feature engineering, statistical methods