Machine Learning, Kafka, Sql, Gcp, Docker, Spark, Kubernetes, Python, AWS, Llm, RAG, Generative AI models

Machine Learning, Kafka, Sql, Tensorflow, Pandas, Numpy, Gcp, Pytorch, Docker, Kubernetes, Python, AWS, LangChain, Airflow, scikit-learn

containerization , Docker, System Design, Python, Asynchronous processing, Generative AI, Event-driven systems, Cloud platforms, Multi-step AI pipelines, Scalable architectures, distributed architectures, Orchestration frameworks, LLM-based applications, Agentic workflows, Model deployment strategies

Kubernetes, python, Tensorflow, Pytorch, aws, mlflow

Pytorch, Python, experiment tracking, DeepSpeed, benchmarking systems, evaluation frameworks, FSDP, reproducibility practices, Compression, model optimization

Spark Streaming, Kinesis, MLops, Aws Services, Pyspark, Kafka, Databricks, Sql, ELT, Etl, Airflow

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

stream processing , Model Armor, A2A SDK, BigQuery Optimization, Google Gen AI SDK for Python, Vertex AI Agent Engine, Cloud DLP