Data Science, Machine Learning, Data Analysis, Data Processing

Sql, Data Science, Python, embeddings, knowledge engineering, enterprise ontologies, big data infrastructure, vector databases, semantic models, semantic retrieval, GenAI systems, enterprise knowledge grounding, RAG pipelines, semantic data systems, applied AI

MLops, AWS, Terraform, Python, Agentic AI, LLMs

Machine Learning, Natural Language Processing, SAS, Sql, Deep Learning, Pandas, Hive, Numpy, Spark, Python, Generative AI, R

attribution modeling , Ml, Machine Learning, Pyspark, Neural Networks, Support Vector Machine, Clustering, Sql, Deep Learning, Databricks, Azure, Python, AWS, Ai, DL, Regression, Tree-based algorithms, Classification, Cloud Systems, Recommender System

Nlp, LangChain, GPU optimization, Airflow, DeepSpeed, Triton Inference Server, speech models, distributed training, multimodal transformer models, MLflow, real-time video processing, Ray, Vector DBs, Horovod, Cv, RAG, KServe

Databricks, Sql, Tensorflow, Deep Learning, Machine Learning, Pytorch, Data Science, Gcp, Sparql, AWS, Python, Azure, cloud environments, semantic data systems, scikit-learn, GQL, semantic models, embeddings, enterprise ontologies, GenAI systems, RAG, semantic retrieval, Statistical Modeling, Cypher, applied AI, enterprise knowledge grounding, graph query languages, ML libraries, vector databases, knowledge engineering, big data infrastructure

Sql, Gitlab, Pytorch, Tensorflow, Python, XGBoost, Plotly, Shiny

snowflake , Sql, Tensorflow, Pandas, Pytorch, Spark, Python, Scikit-learn, vector databases, MLflow, prompt engineering, Agentic AI systems, MLOps practices, MCP Model Context Protocol, LLM-powered applications, Kubeflow, Generative AI LLM Proficiency, RAG pipelines