data engineering , Machine Learning, Sql, Deep Learning, Git, Big Data Technologies, Agile, Databricks, Rest Apis, Python, Data Transformation, GenAI, scikit-learn, MLlib, NoSQL Databases

Machine Learning Algorithms, Deep Learning, Image Processing, MongoDB, Predictive Analytics, Python, Visualization, Linux environments, Model monitoring, Advanced Statistical Modelling, NoSQL databases, Data Processing, Recommendation Systems, R, RDBMS SQL, Large-scale datasets, Data Gathering, Statistical Techniques

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

Tensorflow, System Design, Computer Vision, Python, Pyspark, Torch, Recommendation engines, Graph Knowledge Bases, Large-scale Search systems, Image Understanding models, Generative AI

data engineering , Machine Learning, Python, Sql, Data Analysis, Causal AI, Structured Causal Models

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

Data Cleansing, data structures, test strategy development, propensity models, AI ML modeling, performance validation, recommendation engines

Pyspark, Machine Learning, Data Mining, Excel, Python, Sql, AWS, R, Rspark, Statistical Pattern Recognition