software engineering practices , data wrangling , Python, Machine Learning Algorithms, Advanced Analytics, Analytical pipelines, Machine learning libraries, Mathematical Modeling, Statistics

Sql, Python, experimentation, Modeling

Retrieval-Augmented Generation (RAG), Python, Sql, ML Operations, Generative AI, R, LLM Operations, Embeddings, Metadata filters, Vector databases, Chunking, Data Collection, and pre-processing, Data science pipelines, Cleaning

Predictive Modelling, Sql, Python, graph analytics, experimentation frameworks, anomaly detection

Tensorflow, Pytorch, Pandas, Gcp, MLops, Azure, Python, AWS, Predictive Time Series modelling, Time Series modelling, scikit-learn

Tensorflow, Numpy, Machine Learning, Pandas, Pytorch, XGBoost, Python, Sql, MLops, scikit-learn

Databases, Machine Learning, Sql, Python, Data Pipelines, Model Deployment, Analytics Tools, Statistics

S3, Tensorflow, Lambda, Kinesis, Pytorch, Python, AWS, Sql, Kms, MLops, Iam, Containers, cold start, Amazon SageMaker AI, Thompson Sampling, Hugging Face, vector databases, reinforcement learning, IaC, LinUCB, contextual bandits, off-policy evaluation, observability, LangChain, reward design, Step Functions, Exploration, Glue, Athena, CI CD for ML

BigQuery, Power Bi, Tableau, Sql, Databricks, Predictive Analytics, Statistical Analysis, Python, Generative AI, CPLEX, OR-Tools, Looker, Optimization, Vertex AI, Gemini, Gurobi, Large Language Models