Description

You will architect a scalable, GenAI-powered remediation platform on Google Cloud Platform.

This role is on-site.

Responsibilities

• Design end-to-end system architecture for data ingestion from Mainframe (z/OS), AS400, and Splunk into a Common Information Model.
• Establish BigQuery as the centralized data foundation and implement efficient ELT/ETL pipelines.
• Engineer Human-in-the-Loop workflows for low-confidence incident handling and closed-loop feedback mechanisms.
• Implement row-level security and data masking to meet Healthcare regulatory compliance.
• Oversee LLM and MLOps lifecycle, including retraining triggers and performance monitoring.

Required Skills

• Expert-level proficiency in GCP services: Vertex AI, BigQuery, Dataflow, Pub/Sub, Cloud Run, and Cloud Functions.
• Deep practical experience with RAG architectures, embedding models, and vector database management within BigQuery.
• Strong background in connecting legacy enterprise infrastructure (Mainframe/AS400) to modern cloud data pipelines.
• Proficiency in Python, SQL, PySpark, and Terraform for infrastructure-as-code.
• 5+ years of experience in cloud architecture or data engineering.
• Ability to serve as a technical bridge between stakeholders and engineering teams.

Preferred Skills

• Experience with Healthcare regulatory requirements and data governance.
• Background in normalizing data from diverse legacy sources.

Key Skills

Education

Any Gradute