Forward Deployed Engineer (Generative AI)

Excerpt from the original posting

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 Solut…

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