Forward Deployment Engineer, Generative AI

Tiger Analytics

Forward Deployment Engineer integrating and scaling Generative AI solutions collaboratively with clients. Working closely with engineering teams to operationalize AI models across multi-cloud environments.

Posted 5/18/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

AWSAzureCloudGoGoogle Cloud PlatformKubernetesPythonPyTorchTerraform

About the role

Key responsibilities & impact

The Forward Deployment 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

What you’ll need

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).
Multi-Cloud Management: Build agnostic, resilient cloud deployments across AWS, Azure, and GCP using Infrastructure as Code (IaC).
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.
Technical Requirements- AI Frameworks: Hands-on experience with LLM orchestration tools (LangChain, LlamaIndex, AutoGen) and deep learning frameworks (PyTorch, Hugging Face).
Vector Databases: Production experience setting up and querying vector stores (Milvus, Pinecone, Qdrant, Chroma, or pgvector).
Model Operations (LLMOps): Proficiency in model serving frameworks (vLLM, TGI, Triton Inference Server) and evaluation tools.
Cloud & Containers: Advanced knowledge of cloud AI primitives (AWS Bedrock/SageMaker, Azure OpenAI, GCP Vertex AI) and Kubernetes (K8s) for GPU workloads.
IaC & Automation: Mastery of Terraform or OpenTofu to provision complex multi-cloud compute environments.
Programming: Strong coding skills in Python (preferred) or Go, with an emphasis on writing clean, concurrent code.
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.

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.
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.

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Hard Skills & Tools

Generative AI solutionsLarge Language Models (LLMs)GPU orchestrationTPU orchestrationdata ingestion pipelinesVector Database architecturesInfrastructure as Code (IaC)LLM orchestration toolsdeep learning frameworksmodel serving frameworks

Soft Skills

analytical skillstechnical advocacycustomer expectation managementrapid adaptabilitycritical debuggingcollaborationcommunicationproblem-solvingleadershipconsultation