data engineering , Large Language Models (LLMs), Statistical Analysis, Machine Learning, Data Governance, Sql, MLops, Python, Prescriptive modeling, Semantic Layers, Feature engineering, Agent orchestration frameworks, Embeddings, LLM APIs, Real-time anomaly detection models, ML pipelines, RAG, Large-scale data processing systems, AI agents

Svm, Deep Learning, Neural Network, Predictive Modeling, Clustering, Python, applied statistical analysis, Forecasting, Random Forests, R, Regression, Boosting, Tree, text analysis

Tensorflow, Pandas, Pytorch, Hadoop, Spark, Python, Sql, Google Cloud, AWS, Scikit-learn

Predictive Modeling, Machine Learning, Svm, Clustering, Deep Learning, Statistical Analysis, Python, Forecasting, Random Forests, Regression, R, Boosting, Text Analysis, Tree

causal inference , Databricks, Clustering, Pandas, Regression Analysis, Numpy, Python, Hypothesis Testing, Scipy, Bayesian Methods, scikit-learn, statistical methodology, stats models, Survival Analysis, Experimental Design, Simulation, time-series analysis

Unix, Sql, Shell scripting, Hive, Java, Python, Data Visualization, Linux, R

Tensorflow, Pytorch, Python, Graphql, Neo4j, graph-based data modeling, PQ, metadata filtering, Nemotron, natural language query answering, FAISS-based vector indexes, 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