Tensorflow, MLops, Pytorch, Python, inference efficiency, model versioning, GPU acceleration, GAN, automated retraining systems, evaluation pipelines, Generative Video AI, model optimization

Java, Postgres, Kafka, Python, Hana, Go, GRPC, AI ML frameworks

Git, Pytorch, Linux, Python, Cuda, NVIDIA, AMD, Intel

Python programming for ML and automation, MLOps practices and ML lifecycle management, Kubernetes for orchestration, Docker for containerization

Algorithms, Hadoop, Kafka, Node.js, Tensorflow, Django, React, Gcp, Pytorch, Docker, Spark, data structures, Azure, Kubernetes, Python, AWS, Airflow, scikit-learn, transfer learning, generative AI technologies, prompt engineering, Vector Databases, RAG architectures

S3, Machine Learning, Pytorch, Docker, Sqlite, XGBoost, Postgres, Python, AWS, Matplotlib, Sql, Jenkins, Pandas, HuggingFace, LabelBox, OpenSearch, ML pipelines, MosaicML, LightGBM, Amazon EKS, Scikit-learn, DVC, NVIDIA NeMo, Transformers, Jupyter

Tensorflow, Java, Springboot, Gcp, Pytorch, Azure, Python, AWS, Google BigQuery, Azure Data Explorer

Git, Docker, Python, data pipelines, Linux-based systems, AWS ML GenAI services, RAG, LLM-based architectures, ML GenAI models

Machine Learning, Deep Learning, Python, distributed AI systems, AI engineering, autonomous AI systems, ML system architecture, platform engineering, agent frameworks, AI system architecture, large language models, cloud-native architectures, RAG pipelines