Forward Deployed Engineer, Generative AI

Tiger Analytics

Forward Deployed Engineer at Tiger Analytics driving deployment and scaling of Generative AI solutions. Collaborate with teams to operationalize AI research into production-grade infrastructure.

Posted 7/14/2026full-timeRemote • Texas • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and integrating Generative AI solutions within multi-cloud environments, leveraging tools like Vertex AI and BigQuery for model training and data management. Strong analytical and collaborative skills are essential for translating business goals into effective technical architectures.

Highest-signal resume keywords

Vertex AI MasterySQL Proficiency for BigQueryPython for ML EngineeringCloud Infrastructure ExperienceData Pipeline Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Generative AI SolutionsLarge Language Models (LLMs)Vertex AI Agent BuilderAgent Developer Kit (ADK)Model Context Protocol (MCP) ToolboxDataflow for Streaming Data PipelinesRunInference APIVector EnginesData Preprocessing TechniquesModel Evaluation

Soft Skills

Transparent CommunicationProactive CollaborationConsultative ApproachActive Participation

Tools & Technologies

Google Cloud StorageVertex AI EndpointsBigQueryAlloyDB

Industry Keywords

Multi-Cloud EnvironmentsAI ResearchProduction-Grade InfrastructureReal-Time InferenceAgentic Architectures

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Drive the on-site deployment, integration, and scaling of enterprise Generative AI solutions.
Embed directly within customer engineering teams to operationalize Large Language Models (LLMs) and retrieval systems across multi-cloud environments (AWS, Azure, GCP).
Bridge the gap between AI research and production-grade cloud infrastructure.
Collaborate with cross-functional teams and business partners to leverage analytical skills and ensure business value.

Requirements

What you’ll need

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.
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".
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.
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

Comp & perks

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.