Tensorflow, Pytorch, Python, MLops, Spark, Databricks, GPU infrastructure, model versioning, event tokenization, efficient attention, attention-based architectures, positional embeddings, mixed-precision training, self-supervised pretraining, gradient accumulation, automated retraining, experiment tracking, masked modeling, deep learning models, distributed strategies, contrastive learning, check-pointing, downstream fine-tuning, Production Monitoring

Java, Graphql, PostgreSQL, Sap Hana, Python, Apis, Go, LangGraph, Cloud-native architectures, Data platforms

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

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

triton , Pytorch, FastAPI, Kubernetes, Python, Docker, Restful Apis, Sagemaker, ML technologies, document databases, TensorRT

Distributed Computing, Apache Airflow, Cloud Infrastructure, Linux, Terraform, metrics, System Design, Distributed Systems, Kubernetes, Python, Logging, Infrastructure as Code, Alerting, MLflow, Reliability, Monitoring

Api Development, Logging, Bitbucket, Sql, Deep Learning, Gcp, Python, model performance monitoring, Cloud Build, model lifecycle, feature monitoring, model deploying, model registry, CI CD pipelines

tokenization , Tensorflow, Machine Learning, Pytorch, Natural Language Processing, Objective C, Swift, Python, Deep Learning, NLP algorithms, Text decoding

MLops, Spark, Databricks, Python, Kubernetes, AWS, rollback, deployment strategies, experiment tracking, ML infrastructure, versioning, embedding retrieval systems, classical ML models, Ray, LLM agent workflows, reproducible training data, model validation, model registry, Production Monitoring, NLP models

Github, Sql, Git, MLops, Docker, Databricks, Kubernetes, Python, Orchestration, Testing, Prefect, R, GitHub Actions, CI/CD pipelines, GitOps, Production Monitoring, Software engineering best practices, Model versioning, Containerisation, Unity Catalog, Lineage