Role Overview

The Forward Deployment Engineer (FDE) drives the on-site deployment, integration, and scaling of our enterprise Generative AI solutions. This role embeds directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across multi-cloud environments (AWS, Azure, GCP). You will bridge the gap between AI research and production-grade cloud infrastructure.

You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

Requirements

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 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.

Benefits

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.