Machine Learning Algorithms, Sql, Machine Learning, Python, Reference architectures, Scalable machine learning pipelines, AI engineering, Optimization methodologies, AI ML stack, ML Ops practices, Predictive models, Gen AI, LangGraph, Agentic AI, LLM orchestration frameworks, Large-scale data handling, Agentic workflows, RAG architectures

prophet , Sql, Deep Learning, Tensorflow, Pytorch, Git, XGBoost, Machine Learning, Kubernetes, Python, scikit-learn, Time Series Models, Random Forests, LSTM, permutation importance, Integrated Gradients, statsmodels, SHAP, transformer-based models, counterfactuals, GRU, data visualization tools, Gradient Boosting, Temporal Convolutional Networks, LightGBM, Statistical models

Oracle, Sql, shell scripting, Unix, MySQL, Hive, Java, Python, Data Visualization, Linux, R, Teradata

Pytorch, MLops, AI development in regulated environments, Weakly-supervised learning, Deep learning workflows, Medical image analysis, Multimodal models

Tensorflow, Pytorch, Python, Graphql, Neo4j, PQ, metadata filtering, Nemotron, natural language query answering, FAISS-based vector indexes, graph-based data modeling, Retrieval-Augmented Generation pipelines, vector databases, HNSW, QLoRA, document chunking, LoRA, IVF, Llama variants, Knowledge Graphs, parameter-efficient fine-tuning, Mistral, Phi

Sql, Natural Language Processing, Hadoop, Google Cloud, AWS, Python, Computer Vision, Spark, ML evaluation techniques, LLMs, Transformers, GANs, fine-tuning, deep learning architectures, perceptual quality metrics, diffusion models, R, generative tasks, multimodal learning, user-in-the-loop testing, prompt engineering