Tensorflow, Git, Pytorch, MLops, Docker, Flask, FastAPI, Python, Kubernetes, AWS, Airflow, vector databases, embedding models, Sagemaker, Streamlit

Pytorch, Python, Agreement analysis, Prompting, Instruction tuning, Semantic parsing, Confidence scoring, Output evaluation, Data annotation or labeling systems, Extraction, Classification, Label quality metrics, Evaluation design and quality measurement, Document understanding tasks, Layout analysis

model selection , statistical concepts , Java, Tensorflow, Deep Learning, Pytorch, Keras, Python, Machine Learning Algorithms, probability theory, calculus, evaluating model performance, R, sci-kit, linear algebra, hyperparameter tuning

Cnn, Random Forest, Tensorflow, Numpy, Rnn, Pytorch, Docker, Python, AWS, Logistic Regression, Machine Learning Algorithms, Sql, Git, Pandas, Linear Regression, Decision Trees, Azure, Kubernetes, Gradient Boosting Models, Support Vector Machines, MLOps practices, Google Cloud AI services, Clustering Algorithms, LSTM, Scikit-learn, Deep Learning concepts, Transformers

Java, Graphql, Scala, Kafka, TigerGraph, MLops, Docker, Neo4j, Spark, Python, Kubernetes, AWS, Airflow, pgvector, Qdrant, Go, MLflow, Flink, Neptune, Kubeflow, Milvus

.NET, Java, Phoenix, Tensorflow, MLops, Pytorch, Keras, Python, LangChain, LLMOps, AgentOps, generative models, reinforcement learning, LangGraph, AI methodologies, MLFlow, Google Agent Development Kit

Python, Sql, Adf, Hadoop, Spark, Azure DevOps, MLFlow, Feature Stores

Tensorflow, Numpy, Git, Pandas, Pytorch, Docker, Python, Sql, scikit-learn, cloud environments

regression models , Sql, Deep Learning, Spark, Data Analytics, Python, boosted models, Airflow, classification models, Generative AI, LLMs, MLflow, anomaly detection, simulation problems, AI frameworks