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

Java, Machine Learning, Tensorflow, Pytorch, Python, Large-scale ML systems, Agentic Systems, Diagnostics, Knowledge retrieval, Model versioning, Model training, Feature engineering, Data pipelines, Distributed training, Inference optimizations, Monitoring

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

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

Machine Learning, Cloudformation, Tensorflow, Numpy, Pandas, Pytorch, Terraform, Docker, Kubernetes, Python, AWS, Airflow, scikit-learn, MLflow, SageMaker, DVC

Machine Learning, Gcp, Docker, App Engine, Azure, Kubernetes, AWS, Generative AI, Model versioning, NLP fundamentals, Experiment tracking, Automated retraining

data engineering , Machine Learning, Sql, Deep Learning, Tensorflow, MLops, Pytorch, Data Analytics, Python, Computer Vision, GenAI, CNNs, Transformer-based architectures

triton , Apis, Testing, Pytorch, Distributed Systems, Debugging, Kubernetes, Python, AWS, TensorRT, vLLM, inference optimization techniques, ML systems

snowflake , Machine Learning, Kafka, Sql, Data Science, MLops, Spark, Databricks, Python, Probabilistic modeling, Airflow, Generative AI, LLMs, Feature engineering, Transformers, Tree-based models