ML Architect

MLOps / ML Architect 01 starts August

• 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