process mining , Deep Learning, Nlp, Spark, Databricks, Python, data pipelines, unsupervised ML, ML models, supervised ML

Cloud, Distributed Systems, RAG agentic retrieval, NLP representation learning, AI-native engineering practice, Language runtime fundamentals, Information retrieval ranking, Evaluation experimentation, Search infrastructure at scale

Spark, ML Ops, Applied ML

causal inference , prophet , Sql, Tensorflow, Pytorch, MLops, Databricks, Python, Airflow, data pipelines, scikit-learn, PyMC, Stan, Bayesian modeling, R, dbt, Experimental Design, hierarchical models, feature engineering

Pyspark, Machine Learning, Cicd, Databricks, Azure, Python, Time Series Forecasting, ADF pipelines, Demand Forecasting

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

Sql, Tensorflow, Machine Learning, Pytorch, Python, Distributed data processing, RAG pipelines, Agentic AI systems, Feature engineering, Statistical Models, LLM-powered applications, Prompt engineering, Deep learning frameworks

Tensorflow, Machine Learning, MLops, AWS, Pytorch, Python, Azure, Gcp, Hl7, LLaMA, Claude, OpenAI GPT, Hugging Face, EMR EHR systems, FHIR, AI ML models, Statistical models, Generative AI

Sql, Pytorch, Python, Tensorflow, MLops, scikit-learn

Numpy, Databricks, Sql, Pytorch, Azure Data Factory, Tensorflow, Python, Microsoft Azure, Pandas, Classical Machine Learning, Azure Data Foundry, Generative AI