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

Key Skills

Education

Any Gradute