Data Science, Machine Learning, Data Analysis, Data Processing

statistical concepts , Sql, Predictive Modeling, Neural Networks, Deep Learning, Decision Trees, Machine Learning, Clustering, Python, Model training, Data ingestion, Responsible AI, Explainable AI, Ai, Ensemble methods, Feature engineering

RAG (Retrieval-Augmented Generation) architecture, GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), Data Structures And Algorithms, Python Programming, LLMs, Autoregressive models, SLMs, Model finetuning, AI accelerator chips, Transformer-based networks, Generative AI architectures, MLOps practices, Modern AI frameworks

Nlp, Git, Python, Sql, Gcp, Azure, AWS, LLM fine-tuning

Data Scientist, Python, Tensorflow, MLops, Ms Sql Server, Databricks, PyTorch libraries

Tensorflow, Databricks, AWS, Python, Azure, Sklearn, Pytorch, XGBoost, MLFlow, CPLEX, Gurobi, MLlib

Sql, Computer Vision, Pyspark, Machine Learning, MLops, Python, Docker, Scala, Flask, Spark, PEFT, Personalization Algorithms, RAG, User Behavior Modeling, Recommendation Systems

Ml, Deep Learning, MLops, Python, Algorithms, data structures, generative AI architectures, LLMs, GANs, Transformer-based networks, Ai, DL, SLMs, autoregressive models, Variational Autoencoders, reinforcement learning, RAG, Agentic AI, RL, modern AI frameworks, Generative AI

Tensorflow, Azure, Python, scikit-learn, Data Processing