Machine Learning, Apis, Kafka, Microservices, Tensorflow, Git, Pytorch, Gcp, Docker, Opencv, Spark, Databricks, Azure, Kubernetes, Python, AWS, DevOps MLOps practices, Airflow, data pipelines, MLflow, ONNX, AI-assisted engineering tools, CI CD pipelines

pruning , Hive, Python, XGBoost, Spark, feature engineering, monitoring ML LLM systems, scikit-learn, modern LLM tooling, large-scale data pipelines, LangChain, UDFs, feature selection

snowflake , Version Control Systems, Unix, Hadoop, Apache Spark, Kafka, Windows, Redshift, Sql, Linux, Plsql, Azure Machine Learning, Python, AWS

data preparation , DataEngineer, Feature Engineering, Model Deployment, Senior Data Engineer with AI/ML, S3 Glue EMR Redshift Lambda, ? AWS SageMaker experience, ? Kafka or Kinesis for real-time streaming, ? MLOps and CI/CD for ML pipelines, ? Power BI integration with cloud-based data platforms and large-scale datasets

Microservices, Terraform, Python, AWS, Rust, Cloudformation, Devops, Jenkins, Git, Gcp, FastAPI, Azure, Kubernetes, RAG systems, Hugging Face Transformers, Go, MLflow, TF Serving, vector database management, GitHub Actions, TorchServe, LangChain, big data processing using Spark, vLLM, test-driven development, semantic search implementation, model quantization techniques, CI CD pipelines

Pandas, Pytorch, Python, Tensorflow, Nlp, MLops, model registry, scikit-learn, ML ecosystem, experiment tracking

Scala, Gitlab, Hive, Java, Openshift, Python, Kubernetes, Jenkins, Apache Kafka, Spark

Docker, Python, Linux stack

Machine Learning, Big Data, Sql, Deep Learning, Tensorflow, Cloud Computing, Pandas, Pytorch, XGBoost, Data Visualization, Python, Statistical Analysis, Scikit-learn, MLOps pipeline