Pyspark, Random Forest, Sql, Git, Docker, XGBoost, Databricks, Python, LightGBM, MLflow, Scikit-Learn, SageMaker

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

Numpy, Pytorch, Python, Tensorflow, Pandas

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

PostgreSQL, Rdf, Neo4j, Python, AWS, Apis, Gcp, MLops, Azure, pgvector, LLM-driven applications, vector databases, Pinecone, GenAIOps, LangGraph, Chroma, GenAI microservices, RAG pipelines, architecture standards, LangChain, knowledge-grounding frameworks, cloud platforms, prompt engineering, knowledge graph systems, agentic AI systems, Transformers, Milvus, Weaviate, LlamaIndex

Servicenow, Apis, LangChain, CrewAI, Email, Hugging Face, vector databases, Azure OpenAI, AutoGen, multi-agent workflows, LLM models, GenAI Systems, Teams, Sharepoint, OpenAI

Unsupervised Learning, Spark, Databricks, Python, Deep Learning, supervised learning

graph databases , Databricks, Tensorflow, MLops, Machine Learning, Pytorch, Neo4j, Advanced Analytics, Keras, AWS, Spark SQL, Python, Azure, Nlp, LangChain, LLMs, LlamaIndex, DGL, LLMOps, embeddings, RAG, model lifecycle management, knowledge graphs, Scikit-learn, vector databases, semantic search, generative AI, prompt engineering

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