graph databases , Data Manipulation, API design, Sql, Nosql, Typescript, Docker, Python, AWS, LangChain, vector databases, data lakes, Statistics, feature engineering, synthetic data generation

graph databases , Tensorflow, Nlp, Pytorch, Gcp, Neo4j, Azure, Python, AWS, GenAI, knowledge graphs, LLMs, Scikit-learn, DGL, cloud AI ML services, vector stores, MLOps practices

graph databases , Tensorflow, Nlp, Pytorch, Gcp, Neo4j, Azure, Python, AWS, GenAI, knowledge graphs, LLMs, Scikit-learn, DGL, cloud AI ML services, vector stores, MLOps practices

snowflake , Machine Learning, Tensorflow, Nlp, Pytorch, Docker, Azure DevOps, Sql, Deep Learning, Keras, Kubernetes, MLflow, LangGraph, Vector Databases, GitHub Actions, Azure AI ecosystem, Azure AI Search, RAG architectures, LangChain, Generative AI, Semantic Kernel, Agentic AI, Statistical Modeling, Embedding Models

AWS, Python, Azure, Gcp, Git, Pyspark, Machine Learning, MLops, Generative AI, Hugging Face Transformers, LangChain, vector databases

Sql, Tensorflow, Pandas, Python Programming, Numpy, Pytorch, Scipy, vector databases, LLMs, model deployment workflows, scikit-learn, statsmodels, GenAI technologies, RAG, LangChain, MLOps practices, prompt engineering

Kubernetes, Ml, Sql, Cloud, Apache Spark, Kafka, MLops, AWS, Nosql, Python, Azure, Docker, Gcp, Vector Databases, LangChain, pgvector, Pinecone, LLM Frameworks