Google Cloud AI Engineer

MethodHub; Mountain View (CA); On-site; USD 120,000 - 190,000; Full time

Job summary

MethodHub is seeking a highly skilled Artificial Intelligence Engineer to design and deploy intelligent agents integrated with business data to drive trust and operational efficiency. This role emphasizes hands-on design of agentic architectures, end-to-end implementation, and proactive collaboration with cross-functional teams to translate business goals into scalable technical solutions.

Ideal candidates combine cloud data engineering with ML optimization and a strong privacy mindset for

Qualifications

• Vertex AI Mastery: Proven experience with Model Garden, Vertex AI Pipelines, and model evaluation.
• Data Proficiency: SQL for BigQuery, Python for ML engineering, and data preprocessing techniques.
• Cloud Infrastructure: Hands-on with Google Cloud Storage and Vertex AI endpoints.
• Emerging Tech: Familiarity with stateful real-time processing and agentic architectures.

Responsibilities

• Design and deploy intelligent agents to automate complex business workflows.
• Leverage ADK to build and manage multi-agent systems that collaborate to solve end-to-end challenges.
• Connect agents to enterprise data sources like BigQuery and Spanner via MCP Toolbox.
• Ground AI models in live business context using vector engines within BigQuery/ AlloyDB.

Tools

Vertex AI Mastery

BigQuery

Python (ML)

Dataflow

Cloud Storage

Vertex AI Endpoints

Job description

We are seeking a highly skilled Artificial Intelligence Engineer. 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 Run Inference 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 Qualifications

• 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.