GCP Architect

GCP Architect
Location: Dallas Texas - Hybrid - 3 Days a week Better to look for Local Candidates
6 Months

Job Description

• System Architecture: Architect the end-to-end design of a scalable GenAI-powered remediation platform on GCP. Design ingestion patterns to normalize data from Mainframe (z/OS) AS400 and Splunk into a Common Information Model (CIM).
  BigQuery Data Foundation: Establish BigQuery as the centralized source of truth. Design and implement efficient ELT/ETL pipelines and utilize BigQuery Vector Search for RAG (Retrieval-Augmented Generation) workloads.
  Human-in-the-Loop (HITL) Workflow: Engineer the critical workflow for Low Confidence incident handling. Ensure seamless integration between AI-generated hypotheses and expert analyst resolution creating closed-loop feedback mechanisms that improve model accuracy over time.
  Governance & Compliance: Implement row-level security (RLS) and data masking to meet Healthcare regulatory requirements while providing LLMs the context needed for inference.
  Model Lifecycle & MLOps: Oversee the LLM and MLOps lifecycle managing retraining triggers based on verified analyst resolutions model evaluation and performance monitoring.
• Technical Qualifications
  Cloud Platform: Expert-level proficiency in GCP (Vertex AI BigQuery Dataflow Pub/Sub Cloud Run Cloud Functions).
  GenAI & RAG: Deep practical experience with RAG architectures embedding models and vector database management (specifically within the BigQuery ecosystem).
  Legacy Integration: Strong background in connecting legacy enterprise infrastructure (Mainframe/AS400) to modern cloud data pipelines.
  Engineering Practices: Proficiency in Python/SQL PYSPARK and infrastructure-as-code (Terraform) for reproducible automated deployment.
  Communication: Ability to serve as a technical bridge explaining complex AI trade-offs to stakeholders while providing clear guidance to engineering teams.