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Tiger Analytics

Forward Deployed Engineer (Generative AI)

Remote Mid Posted July 14, 2026

Role 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.*

About Forward Deployed Engineering

Forward Deployed Engineers are embedded directly with customers to build custom solutions, integrate products into existing infrastructure, and bridge the gap between product engineering and customer success. The role combines deep technical skills with the ability to operate in client environments and translate business requirements into working software.

Originally pioneered by Palantir, the FDE model has spread across AI, enterprise SaaS, and cloud infrastructure companies. FDEs write production code, architect integrations, train customer teams, and feed product insights back to the core engineering organization. At companies like OpenAI, Salesforce, and Databricks, FDE teams are treated as elite engineering units that can ship custom solutions in days rather than quarters.

Typical FDE stack: Python, TypeScript, SQL, REST/GraphQL APIs, cloud platforms (AWS/GCP/Azure), and increasingly LLM APIs and AI orchestration frameworks. Strong communication and the ability to context-switch between technical and business conversations are as important as coding ability.

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