AI Engineer

Implement tools like **ClientP (Model Context Protocol) Toolbox** to securely connect agents to enterprise databases such as BigQuery and Spanner. - **Data Proficiency:** Advanced knowledge of SQL for BigQuery, Python for ML engineering, and data preprocessing techniques (scaling, encoding, imputation).

Role Name: AI Engineer
Work site: (Remote)

AI Engineer

**Key Responsibilities:**

**1. 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 **ClientP (Model Context Protocol) Toolbox** to securely connect agents to enterprise databases such as BigQuery and Spanner.

**2. 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.