Collaborate: Work with teams across the business to identify learning needs, gather subject matter expertise, and facilitate structured learning initiatives.
For large-model training and evaluation scenarios, participate in formulating and continuously iterating data quality standards, judgment rules, and acceptance criteria.
Design and deliver end-to-end AI and data solutions using leading hyperscaler ecosystems (AWS, Azure, Google Cloud) and modern data/AI platforms (Salesforce, NVIDIA, Databricks, Snowflake etc).
Design, build, and maintain ML infrastructure and tooling to support the full ML lifecycle (data preparation, training, evaluation, deployment, monitoring, and retraining).
Design, build, and maintain end-to-end machine learning pipelines on Databricks, covering data preparation, feature engineering, model training, evaluation, and deployment.