Job Description

Role Purpose

The purpose of the role is to create exceptional and detailed architectural application design and provide thought leadership and enable delivery teams to provide exceptional client engagement and satisfaction.

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Key Responsibilities and Skillsets:

• End-to-End ML Strategy: Lead the architectural design of the ML lifecycle, making critical decisions between AutoML for rapid time-to-market and Custom ML (Keras, PyTorch, XGBoost) for specialized healthcare or transactional use cases.
• Orchestration & Pipelines: Build and manage production-grade ML pipelines using Vertex AI Pipelines (Kubeflow) or equivalent frameworks (AWS SageMaker Pipelines, Azure ML Pipelines, or Airflow) to automate data ingestion, preprocessing, and model retraining.
• Data-to-Model Integration: Design high-performance feature engineering workflows within Cloud Data Warehouses, specifically leveraging BigQuery ML (BQML) or equivalents like Snowflake/Databricks for in-warehouse modeling on massive structured datasets.
• Distributed Computing: Implement scalable feature processing and training strategies using Vertex AI on Ray, Spark, or Dask to handle large-scale data joins across disparate sources (e.g., joining millions of healthcare claims with terabytes of geospatial and weather data).
• MLOps & Lifecycle Management: Establish enterprise MLOps standards, including model versioning via Vertex AI Model Registry (or MLflow), CI/CD for ML using Cloud Build, and automated monitoring for data drift and model performance decay.