14 16 years of total experience, 3 5 years of relevant experience on Sagemaker AI
Hands on experience of the following
Structure a ML system (batch, real-time, or LLM) as modular ML pipelines that can be independently developed, tested, and operated
Ensure the consistency of feature data between offline training and online operations
Govern data in a feature store and promote collaboration between teams with a feature store
Implement MLOps principles of automated testing, versioning, and monitoring of features and models.
The modeling skills required for ML:
How to train ML models from (time-series) tabular data in a feature store
How to personalize LLMs using fine-tuning and RAG
How to validate models using evaluation data from a feature store
Identify and develop reusable model-independent features
Identify and develop model-dependent features
Identify and develop on-demand (real-time) features
validate feature data, test feature functions and test ML pipelines
Schedule feature pipelines and batch inference pipelines
Deploy real-time models, connected to a feature store
Log and monitor features and models with a feature store
Develop user-interfaces to ML systems
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Data Science & Machine Learning - OtherEmployement Type: Full time