GCP Architect

American IT Systems • Dallas, TX

21h • Hybrid

About The Position

We are seeking a skilled GCP Architect to design and implement a scalable, GenAI-powered remediation platform on Google Cloud Platform (GCP). This role involves architecting end-to-end solutions, establishing a BigQuery data foundation, engineering human-in-the-loop workflows, implementing robust governance and compliance measures, and managing the MLOps lifecycle for AI models. The ideal candidate will have expert-level proficiency in GCP services, deep experience with GenAI and RAG architectures, and a strong background in integrating legacy systems with cloud data pipelines.

Requirements

• Expert-level proficiency in GCP (Vertex AI, BigQuery, Dataflow, Pub/Sub, Cloud Run, Cloud Functions).
• Deep practical experience with RAG architectures, embedding models, and vector database management (specifically within the BigQuery ecosystem).
• Strong background in connecting legacy enterprise infrastructure (Mainframe/AS400) to modern cloud data pipelines.
• Proficiency in Python/SQL, PYSPARK and infrastructure-as-code (Terraform) for reproducible, automated deployment.
• Ability to explain complex AI trade-offs to stakeholders.
• Ability to provide clear guidance to engineering teams.

Responsibilities

• 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).
• Establish BigQuery as the centralized source of truth.
• Design and implement efficient ELT/ETL pipelines and utilize BigQuery Vector Search for RAG workloads.
• Engineer the critical workflow for "Low Confidence" incident handling, ensuring seamless integration between AI-generated hypotheses and expert analyst resolution.
• Create closed-loop feedback mechanisms that improve model accuracy over time.
• Implement row-level security (RLS) and data masking to meet Healthcare regulatory requirements.
• Oversee the LLM and MLOps lifecycle, managing retraining triggers based on verified analyst resolutions, model evaluation, and performance monitoring.
• Serve as a technical bridge, explaining complex AI trade-offs to stakeholders while providing clear guidance to engineering teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed