Tensorflow, Algorithms, Pytorch, data structures, Scikit-learn, NLP techniques

Large Language Models (LLMs), Java, Microservice architecture, Python, Evaluation, Generative AI, Prompt engineering, LLM evaluation and observability pipelines, SaaS delivery, Fine-tuning, RAG architectures, MLOps principles

snowflake , S3, Pyspark, AWS Glue, Kafka, Docker, Data Governance, Python, BigQuery, Collibra, Redshift, Sql, MLops, Spark, Kubernetes, Airflow, Purview, Alation, Great Expectations, MLflow, Feast, GitHub Actions, Apache Atlas, Vertex AI, Kubeflow, Evidently, DataHub, SageMaker, dbt, GCS

Java, Gcp, Azure, Kubernetes, Solid understanding of DevOps and MLOps principles and practices, Container technologies Docker, SaaS delivery based on microservice architecture, Experience designing or operating LLM evaluation and observability pipelines, Proficiency in Python, Cloud deployment on AWS, RAG architectures

Java, Machine Learning, Data Mining, Deep Learning, Tensorflow, Pytorch, MLops, Pattern Recognition, Python, Random Forests, Model Training Pipelines, Recommendation Systems, Model Monitoring, Gradient Boosting Machines, Learning to Rank

Azure Databricks, XGBoost, Spark, Python, Etl, scikit-learn, governance frameworks, data quality validation, pipeline orchestration, model performance tracking, Production Monitoring, cloud data platforms, drift detection, feature engineering

Pytorch, TVM, simulator-based prototyping, MLIR, neural network architectures, ML compilers, TensorFlow Lite, ONNX Runtime, on-device inference toolchain development, AI ML algorithms, quantization, model optimization

data preparation , Machine Learning, Python, Statistical Analysis, Feature Engineering, Mathematical Modeling, Cloud Environments

Sql, Deep Learning, XGBoost, Mta, Python, scikit-learn, LightGBM, cloud GCP, MMM, CLV, Optimisation, production ML, propensity