Google Cloud Platform Enterprise Architect

Role Overview:
Looking for senior AI/ ML architect to lead the end-to-end design and orchestration of high-scale machine learning solutions. You will be responsible for marrying complex datasets (e.g., Healthcare, Geospatial, and Weather) into actionable predictive models. While expertise in Google Cloud is preferred, we value deep architectural experience in AWS SageMaker, Azure ML, or Databricks, provided you can translate those concepts into production-grade Vertex AI environments.

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.
• Advanced Evaluation & Explainability: Define rigorous evaluation frameworks beyond simple accuracy, focusing on industry-specific metrics (e.g., Recall/F1 for healthcare) and implementing Explainable AI (XAI) to provide human-readable justification for model predictions.
• Security & Governance: Architect solutions within secure perimeters (e.g., VPC Service Controls) to meet strict compliance standards (HIPAA/PHI), ensuring least-privilege IAM access and secure service-account impersonation across the ML stack.
• Modeling Environments: Manage and standardize collaborative development environments using Colab Enterprise or Vertex AI Workbench, ensuring seamless transition of Python code from local prototyping to scalable cloud execution.
• Cross-Functional Integration: Collaborate with AppDev teams to expose ML models as secure, low-latency REST APIs on Vertex AI Endpoints, integrating them into custom-built AI Agent interfaces (Cloud Run/Firebase).
• Technical Mentorship & Interpersonal Skills: Act as the bridge between Data Engineering and DevOps, mentoring ML Engineers on cloud-specific optimizations and best practices for distributed model training. Should be interacting with the customer and internal stakeholders on overall project details across various areas