Svm, Deep Learning, Tensorflow, Pytorch, XGBoost, Spark, Python, CrewAI, GANs, LSTM, Generative AI, RNNs, Diffusion Models, GRU, CNNs, AutoGen, LangGraph, Agentic AI, Transformers, Graph Neural Networks

pruning , Neural Networks, Tensorflow, Pytorch, Docker, Python, Machine Learning Algorithms, Image Processing, AI ML, Deep Learning, Computer Vision, Opencv, model training, TensorRT, knowledge distillation, hyperparameter optimization, RCNN, quantization, model optimization techniques, ML Flow, Scikit-learn, deep learning architectures, RTDETR, YOLO, feature engineering

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