AI & DATA SME – GENAI / RAG / GOOGLE CLOUD

WE’RE HIRING | AI & DATA SME – GENAI / RAG / GOOGLE CLOUD

Looking for a senior AI & Data SME with deep expertise in enterprise data engineering, GenAI, RAG, and Google Cloud data services to support an enterprise Agent factory/AI platform.

📍 Anywhere in USA | Remote
💼 Contract Position

Key Requirements:
• 9–12+ years in Data engineering/data architecture with recent GenAI/RAG delivery
• Strong Enterprise Data Engineering | ETL/ELT | Data Modeling | Data Quality
• Hands-on experience with Google Cloud Data Stack
• BigQuery | Vertex AI Search | AlloyDB / Cloud SQL | Dataflow | Dataproc
• Strong expertise in RAG Architecture | Embeddings | Vector Databases | Chunking | Hybrid/Semantic Search
• Experience with RAG Grounding & Retrieval Quality Evaluation
• Enterprise data integration experience with Databricks | ServiceNow | Snowflake | SharePoint / OneDrive | Adobe | PDFs
• Strong Python & SQL skills with solid understanding of GenAI / LLM data patterns
• Experience with AI Data Governance | PII | DLP | Data Privacy | Data Lineage | Access Management
• Ability to architect data pipelines from enterprise sources into Google Cloud
• Strong understanding of AI data readiness, knowledge design, and grounded AI responses
• Experience advising architects and developers on data architecture and AI/ML workloads
• Strong communication skills with experience engaging senior technical stakeholders

Key Responsibilities:
• Define the enterprise data-to-AI strategy for ingestion, normalization, embeddings, vector stores, and RAG grounding.
• Architect and oversee data pipelines from enterprise source systems into Google Cloud.
• Own retrieval quality, including chunking strategies, embedding models, hybrid search, and grounding accuracy.
• Establish and enforce AI data governance, privacy, security, lineage, and data quality standards.
• Guide model selection and cost/performance optimization for data-intensive AI agent workloads.
• Partner with architects, developers, and engineering teams on data readiness and AI knowledge design.
• Serve as a senior technical authority in stakeholder discussions around AI, data architecture, and platform dependencies.
• Mentor Data & AI Engineers and establish standards for reusable data assets and AI-ready knowledge.

Good to Have:
• Knowledge Graphs | GraphRAG | Corrective RAG / Self-RAG
• Snowflake / Databricks Certifications
• Google Professional Data Engineer certification
• AI evaluation frameworks and observability  

Ideal Candidate:
A senior AI & Data Architect / SME who can bridge enterprise data engineering and GenAI, with hands-on expertise in Google Cloud, RAG, data pipelines, governance, and AI-ready data architecture.

Interested? Send your updated resume today.