Google Cloud Platform Architect

We are looking for a highly experienced Google Cloud Platform Architect to design and architect enterprise-scale GenAI and RAG-based remediation platforms on Google Cloud. The ideal candidate will define end-to-end cloud architecture, establish BigQuery as the enterprise data foundation, and architect secure, scalable AI workflows integrating legacy systems, modern data platforms, and healthcare-compliant governance frameworks.

This role requires deep expertise in enterprise architecture, AI platform strategy, MLOps, and cloud-native modernization., * Architect end-to-end GenAI remediation platforms leveraging Vertex AI, BigQuery, and Google Cloud Platform-native services.

• Define enterprise data architecture and establish BigQuery as the centralized source of truth.
• Design scalable ingestion and normalization frameworks for Mainframe (z/OS), AS400, Splunk, and distributed enterprise systems.
• Architect RAG pipelines using embedding models, vector search, and BigQuery Vector Search capabilities.
• Design Human-in-the-Loop (HITL) frameworks integrating analyst feedback into AI model retraining pipelines.
• Define secure healthcare-compliant AI architectures with row-level security, data masking, encryption, and governance controls.
• Lead MLOps strategy including model lifecycle management, evaluation, observability, retraining triggers, and performance monitoring.
• Create reusable cloud architecture standards, deployment patterns, and Terraform-based automation frameworks.
• Provide architectural leadership across engineering, AI, infrastructure, security, and business teams.
• Present technical roadmaps, architecture decisions, and AI trade-offs to executive leadership and stakeholders.

• 12+ years of enterprise architecture and cloud engineering experience.
• Expert-level knowledge of Google Cloud Platform services including:

• Vertex AI
• BigQuery
• Dataflow
• Pub/Sub
• Cloud Run
• Cloud Functions

• Deep expertise in GenAI, RAG architectures, embeddings, vector databases, and LLM integration.
• Strong experience designing enterprise-scale data platforms and modernization programs.
• Experience integrating legacy Mainframe (z/OS), AS400, and enterprise operational systems into cloud ecosystems.
• Strong expertise in Python, SQL, PySpark, and Terraform.
• Experience implementing MLOps and AI governance frameworks.
• Strong leadership, communication, and stakeholder engagement capabilities.