Machine Learning, Pyspark, Databricks, Sql, Python, Deep Learning, Cloud Systems, Large Data Sets, Ai, Data Platforms

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

Git, Machine Learning, Python, Sql, Agentic AI solutions

Cloud, Distributed Systems, RAG agentic retrieval, NLP representation learning, AI-native engineering practice, Language runtime fundamentals, Information retrieval ranking, Evaluation experimentation, Search infrastructure at scale

Spark, ML Ops, Applied ML

Azure Databricks, Tensorflow, Pytorch, MLops, Python, LangChain, LLM application development, reinforcement learning, CNNs, prompt engineering, LangGraph, enterprise AI security, RAG, NLP techniques, Vision Transformers, Transformer architectures

Hadoop, Natural Language Processing, Sql, Tensorflow, Pytorch, Gcp, MLops, Docker, Spark, Keras, Azure, Python, Kubernetes, AWS, LangChain, DSPy

causal inference , prophet , Sql, Tensorflow, Pytorch, MLops, Databricks, Python, Airflow, data pipelines, scikit-learn, PyMC, Stan, Bayesian modeling, R, dbt, Experimental Design, hierarchical models, feature engineering

matching , Sql, Statistical Modelling, Pyspark, BigQuery, Python, data quality checks, feature engineering, GenAI, reusable analytics datasets, orchestration frameworks, embeddings, agentic automation, enterprise ML data tooling, model scoring processes, retrieval augmented generation, anomaly detection, Optimization, vector databases, Regression, data pipelines, Classification, distributed compute, large-scale data processing, model evaluation, dbt, cloud data platforms, model monitoring, prompt engineering, Forecasting

Deep Learning, Github, Pandas, Numpy, MLops, AWS, Python, Scipy, Azure, Gcp, Git, Learning-to-Rank, Semantic Retrieval, Personalization Models, Search Relevance Techniques, Generative AI