Forward Deployed Engineer (Generative AI)

Tiger Analytics; Mountain View (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Tiger Analytics is seeking an experienced Forward Deployed Engineer (Generative AI) to embed with customer teams and operationalize large-scale Gen AI solutions on Google Cloud Platform. You will drive on-site deployment, integration, and optimization of Vertex AI primitives and vector search across client environments.

The role requires deep knowledge of LLM orchestration, MLOps tooling, Kubernetes for GPU/TPU workloads, and Terraform for secure GCP provisioning.

Qualifications

• Experience deploying Generative AI at scale in enterprise environments.
• On-site deployment and integration with customer teams.
• Willingness to travel to client sites as required.

Responsibilities

• Deploy and optimize Gen AI models within customer environments.
• Architect scalable AI infrastructure for GPU/TPU workloads.
• Design high-throughput data and retrieval pipelines for RAG.
• Advise on AI safety, prompting patterns, and cost optimization.
• Collaborate with product teams to feed deployment insights back to R&D.

Skills

GCP Vertex AI

LLM orchestration

Kubernetes

Python

Tools

Vertex AI Studio

Model Registry

Vertex AI Pipelines

Kubeflow

Milvus

Pinecone

pgvector

vLLM

Triton Inference Server

GKE

Terraform

Google Cloud SDK

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 Google Cloud Platform(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.

Technical Requirements

• GCP & Vertex AI Architecture: Advanced knowledge of Vertex AI primitives, including Vertex AI Studio, Model Registry, Endpoint deployment, Vertex AI Pipelines (Kubeflow), and Vertex AI Vector Search.
• AI Frameworks: Hands-on experience with LLM orchestration tools (LangChain, LlamaIndex, AutoGen) and deep learning frameworks (PyTorch, Hugging Face) optimized for GCP infrastructure.
• Vector Databases: Production experience setting up, optimizing, and querying Vertex AI Vector Search, or managed vector stores like Milvus, Pinecone, and pgvector (Cloud SQL/Spanner).
• Model Operations (LLMOps): Proficiency in model serving frameworks (vLLM, TGI, Triton Inference Server) deployed via Vertex AI or GKE, alongside robust automated model evaluation pipelines.
• Containers & Kubernetes: Deep expertise in Google Kubernetes Engine (GKE) for managing GPU/TPU workloads, autoscaling, and scheduling.
• IaC & Automation: Mastery of Terraform to provision secure, complex GCP environments, IAM roles, and Vertex AI resources.
• Programming: Strong coding skills in Python (preferred) or Go, with an emphasis on writing clean, concurrent code and utilizing the Google Cloud SDK.

Key Responsibilities

• AI Solution Deployment: Deploy, fine-tune, and optimize large-scale Gen AI models and LLM orchestration frameworks within customer cloud environments.
• Infrastructure Engineering: Architect scalable infrastructure for AI workloads utilizing GPU/TPU orchestration, high-performance storage, and low-latency networking.
• Data & Retrieval Pipelines: Design and implement high-throughput data ingestion pipelines and Vector Database architectures for Retrieval-Augmented Generation (RAG).
• Technical Advocacy: Act as the primary technical consultant, guiding enterprise clients through AI safety, prompt engineering patterns, and inference cost optimization.
• Product Collaboration: Feed edge-case deployment insights back to core AI research and platform engineering teams to improve product robustness.

Soft Skills

• AI Consultation: Ability to manage customer expectations around LLM non-determinism, hallucinations, and performance trade-offs.
• Rapid Adaptability: Passion for keeping pace with the weekly advancements in the Generative AI landscape.
• Critical Debugging: Exceptional skill in isolating errors across complex software layers, from GPU drivers up to prompt engineering logic.
• Mobility: Willingness to travel to client sites to lead high-stakes, on-site deployment sprints.

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.