causal inference , Git, Databricks, Python, double debiased ML, uncertainty quantification, price elasticity estimation, heterogeneous treatment effects, scikit-learn, Experimental Design, optimization logic

triton , Jax, Pytorch, Python, Recommender Systems, BigQuery, Deep Learning, Cuda, Spark, Low-latency inference serving, Multimodal Models, Mixed precision, Language Models, Distributed GPU training, Megatron-style frameworks, Data tensor and pipeline parallelism, TensorRT-LLM, DeepSpeed, Triton Inference Server, Large-scale GPU training, vLLM, Model compression, Deep Reinforcement Learning, FSDP

Natural Language Processing (NLP), causal inference , Machine Learning, Apis, Unsupervised Learning, Sql, Tensorflow, Pytorch, Python, scikit-learn, Hugging Face, Ai, Root-Cause Analysis, Time-Series Analysis, anomaly detection, Clustering Techniques, Model Versioning, Statistical Modeling, Experiment Tracking, Cloud Environments

Machine Learning, Python, Sql, Deep Learning

Machine Learning, Pyspark, Big Data, Sql, Deep Learning, Tensorflow, DataBricks, Jenkins, Git, Azure Data Factory, Keras, Python, Spark MLLib

Ml, Nlp, MLops, Predictive Modeling, Python, Sql, GenAI, Optimization, RWD analytics, LLM-development libraries

data preparation , regression models , Predictive Modelling, Python, Sql, Model Evaluation

Loss Given Default (LGD), Probability of Default (PD), Exposure at Default (EAD), Sql, Numpy, Pandas, Statistical Modelling, Python, Logistic Regression, transition matrices, Data Analysis, scikit-learn, IFRS 9 ECL modelling, macroeconomic modelling, Survival Analysis, recovery analysis, statsmodels

natural language processing (NLP), large language models (LLMs), Machine Learning, Power Bi, Azure Databricks, Tensorflow, Pytorch, Azure Machine Learning, Python, Azure DevOps, scikit-learn, vector databases, MLflow, cloud-based AI solutions, Azure OpenAI, prompt engineering, model deployment on Azure, Generative AI technologies, MLOps practices, FAISS, Azure AI services, Azure AI Search