containerization , MLops, Distributed Systems, Logging, LLM-based systems, Alerting, Model observability, Vector databases, Drift detection, Scalable inference systems, Machine Learning Engineering, Orchestration patterns, Model training, Cloud-native architectures, Embeddings, Data ingestion, Experiment tracking, cost tracking, ML platform development, tracing, Feature management, Model registry, Monitoring

Keras, Tensorflow, Frameworks, AWS, Pytorch, Python, Azure, Gcp, model evaluation, feature engineering, data preprocessing, cloud-based ML solutions, MLOps practices, model monitoring

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

FastAPI, Pyspark, Sql, Tensorflow, Azure ML, Pandas, Pytorch, XGBoost, Flask, Databricks, Python, Azure DevOps, scikit-learn, LightGBM

Jenkins, Bitbucket, Asana, JIRA, Machine Learning, AWS, MySQL, FastAPI, Python, Docker, Flask, Git, Nlp, MongoDB, Generative AI

Java, Typescript, MLops, AWS, Python, Kotlin, Azure, Gcp, feature engineering, ranking models, Go, model deployment, applied machine learning, ML systems, Monitoring, CI CD pipelines, production ML infrastructure, Evaluation

Scipy, Tensorflow, Machine Learning, Nlp, Pytorch, Keras, Python, Deep Learning, scikit-learn, LLMs Tools, Data Analysis Techniques