Python, Aws, Ml, Jenkins, Docker

Python, ML concepts and evaluation methodologies, ROS2, MLOps tools and practices

Prometheus, Grafana, Sql, Tensorflow, Pytorch, MLops, Pandas, Docker, XGBoost, Spark, Kubernetes, Python, Airflow, LightGBM, Argo-Workflows, Scikit-learn, Trino

Tensorflow, Machine Learning, Pytorch, Opencv, Ocr, Python, Computer Vision, Deep Learning, GenAI, LLMs, PIL

BigQuery, Google Cloud Platform, Kafka, Sql, Tensorflow, Pytorch, Spark, Keras, Python, retraining, CI CD, GitHub Actions, Automated training pipelines, experimentation, model monitoring

Matplotlib, Machine Learning, C, Artificial Intelligence, Tensorflow, Numpy, Pytorch, Keras, Python, Torch, Deep Learning Frameworks, Caffe, Jupyter, Data Analysis

model selection , Java, Natural Language Processing, data mining, Deep Learning, Tensorflow, Pytorch, Data Visualization, Python, Machine Learning Algorithms, R, Statistical Modeling, hyperparameter tuning

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

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

Java, Hadoop, Kafka, Software Design, Kotlin, Sql, Nosql, Rabbitmq, Hive, Distributed Systems, Spark, data structures, Python, Airflow, ML infrastructure, Go, Flink, Databricks Workflow, production-grade platform services, observability, batch data pipelines