Vertex AI Engineer

Position: Vertex AI Engineer

Location: Remote

Duration: 6+ months

Job Description:

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 such as 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 Requirements (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.

Required Technical Expertise:

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.