Google Cloud AI Engineer

Google

Google Cloud AI Engineer

Remote

Google Cloud AI Engineer for Partner Flex

Role Overview

We are seeking a highly skilled Artificial Intelligence Engineer. This role is pivotal in establishing Google s Data Cloud as the essential foundation for the "agentic era". You will be responsible for designing and deploying sophisticated AI agents and grounding them in unique business data to ensure trust and operational efficiency.

Core Responsibilities

Agentic Design & Implementation

Develop intelligent agents using Vertex AI Agent Builder to automate complex business workflows.

Leverage the Agent Developer Kit (ADK) to build and manage multi-agent systems that collaborate to solve end-to-end business challenges.

Implement tools like MCP (Model Context Protocol) Toolbox to securely connect agents to enterprise databases like BigQuery and Spanner.

AI on Data Strategy

Utilize Vertex AI for model training, tuning, and deployment, ensuring seamless integration with BigQuery for feature engineering.

Build and optimize streaming data pipelines (e.g., via Dataflow) to execute real-time inference using RunInference API or Vertex AI endpoints.

Ground AI models in live business context using vector engines within BigQuery or AlloyDB to eliminate "AI amnesia".

Operational Excellence (Soft Skills)

Active Participation: Show up promptly for all internal and client-facing meetings.

Transparent Communication: Provide regular, structured status updates to team members and stakeholders regarding project milestones and technical blockers.

Proactive Collaboration: Demonstrate the ability to ask for help when facing technical hurdles and contribute to a collaborative troubleshooting environment.

Consultative Approach: Navigate corporate environments to translate high-level business goals into robust technical architectures.

Technical Qualiffications

Vertex AI Mastery: Proven experience with Model Garden, Vertex AI Pipelines, and model evaluation.

Data Proficiency: Advanced knowledge of SQL for BigQuery, Python for ML engineering, and data preprocessing techniques (scaling, encoding, imputation).

Cloud Infrastructure: Hands-on experience with Google Cloud Storage and Vertex AI endpoints.

Emerging Tech: Familiarity with stateful real-time processing and the latest innovations in agentic architectures.

Preferred Experience

Background in financial services or retail to better understand industry-specific data logic (e.g., credit risk, royalty forecasting, or search relevance).

Knowledge of privacy and compliance standards for handling PII through masking and redaction.