Forward Deployed Engineer (Generative AI)

Tiger Analytics; Dallas (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

Tiger Analytics is seeking an experienced Forward Deployed Engineer (Generative AI) to join its analytics consulting team. The role embeds within customer engineering teams to operationalize LLMs and retrieval systems across AWS, Azure, and GCP, bridging research and production infrastructure.

You will work with cross-functional teams to deliver business value, communicate progress, and drive strategy using analytical insights to solve real-world challenges in enterprise settings.

Qualifications

• Experience with Vertex AI, including Model Garden, Pipelines, and evaluation.
• Strong SQL and Python for ML engineering and data preprocessing.
• Hands-on cloud infra experience with BigQuery, GCS and Vertex AI endpoints.
• Familiarity with multi-agent architectures and agentic toolchains.

Responsibilities

• Drive on-site deployment and integration of Gen AI solutions across client environments.

Skills

Vertex AI

Python for ML

SQL for BigQuery

Data preprocessing

Multi-cloud deployment

Agent frameworks

Vector engines

Tools

Vertex AI Agent Builder

ADK

MCP Toolbox

BigQuery

RunInference API

Vertex AI Endpoints

Job description

Tiger Analytics is looking for experienced Forward Deployed Engineer (Generative AI) with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.

Role Overview

The Forward Deployed 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.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.