Machine Learning, Python, Sql, Deep Learning frameworks, Model Registries, Statistical Models, Feature Stores, Gradient Boosting

Drift detection (e.g. Evidently), PostgreSQL, Sql, Numpy, Pandas, Docker, XGBoost, Python, scikit-learn, LightGBM, CatBoost, Hyperparameter optimisation, SHAP, AWS-hosted infrastructure, Automated retraining pipelines, Feature engineering, Model evaluation

Machine Learning, Software Development, Python, Sql, Data Analysis

Tensorflow, Sql, Github, Aws Lambda, MLops, Azure Functions, AWS, Pytorch, Kubernetes, Python, Docker, Gcp, Git, Scikit-learn, GCP Functions, Dataform

Sql, Tensorflow, Hadoop, Tableau, Power Bi, Pytorch, Oracle Cloud, Machine Learning Algorithms, Big Data Technologies, Predictive Analytics, Google Cloud, AWS, Python, Azure, Spark, applied modeling workflows, feature engineering, deep learning frameworks, cloud platforms, data preprocessing, decision-support systems, Statistical Modeling, data visualization tools, R, AI-powered solutions, model evaluation, analytics platforms, Generative AI, prompt engineering

Pyspark, Sql, Databricks, Devops, MLops, Powerbi, Python, Azure, SAFe-agile, Foundry, ML libraries, Microsoft Copilot Studio, Generative AI

.NET, Sql, Tensorflow, Pytorch, Java, MLops, Pandas, Data Science, Angular, Scipy, Scala, Blockchain, Internet Of Things, Cloudera, Matplotlib, Numpy, Pyspark, AWS, Artificial Intelligence, Php, Python, Microsoft Azure Cloud, GCP Google Cloud Platform, Scikit-learn, Spark-mllib, Security

Python, Machine Learning, MLops, R, Statistics

Spark, transformer-based models, prompt templates, Airflow, LlamaIndex, Dataiku, SHAP, model architecture design, LIME, Monitoring, LangChain, feature stores, Pinecone, RAG Retrieval-Augmented Generation, model explainability, drift detection, embedding-based semantic search, context length efficiency, scalable APIs, vector databases, data pipelines, ensemble blending, model monitoring, FAISS, Chroma