Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning

data engineering , Numpy, Pandas, Pytorch, Python, Testing and release, scikit-learn, Development processes, ELT data pipelines, ML research methodologies, Build processes, Production ML systems, Source control management, ML pipelines

Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow

Machine Learning, Kafka, Sql, MLops, Docker, Spark, Python, Kubernetes, AWS, Ai, anomaly detection, Real-time data processing, Time-series forecasting

Machine Learning, Sql, Spark, Python, Neural architectures, ML libraries, Regression, Gradient-boosted trees, ranking, Classification, Causal reasoning, Evaluation frameworks

Tensorflow, Pytorch, Python

containerization , MLops, Distributed Systems, Logging, LLM-based systems, Alerting, Model observability, Vector databases, Drift detection, Scalable inference systems, Machine Learning Engineering, Orchestration patterns, Model training, Cloud-native architectures, Embeddings, Data ingestion, Experiment tracking, cost tracking, ML platform development, tracing, Feature management, Model registry, Monitoring

Docker, Pyspark, Pytest, Azure Databricks, Azure Cloud Services, Python, Sql, MLflow

Tensorflow, Machine Learning, Pytorch, MLops, Natural Language Processing, Sql, Python, Deep Learning, Vector Databases, Generative AI, Retrieval-Augmented Generation