Forward-Deployed AI Engineer: GenAI, RAG & Knowledge Graph

Tiger Analytics; United States; Hybrid; USD 170,000 - 230,000; Full time

Job summary

Tiger Analytics seeks a Lead AI Engineer to drive end-to-end AI Engineering for the Luma platform, leading architecture, design, and delivery of enterprise-scale AI solutions. You will directly engage clients, rapid-build POCs/MVPs, and deploy GenAI, RAG, and Knowledge Graph solutions in production.

The role requires hands-on leadership, strong problem-solving, and the ability to operate in ambiguous environments across AWS, Azure, or GCP. Collaboration with customers and teams is essential.

Qualifications

• Neo4j / Knowledge Graph – Mandatory.
• Generative AI / LLM / RAG / Agentic AI.
• Databricks / Spark / PySpark.
• Application Engineering – Python or Go.
• Rapid Prototyping / POC Development.
• Cloud: AWS / Azure / GCP.
• Strong problem-solving and debugging skills.
• Self-driven, customer-focused, and comfortable in ambiguous environments.

Responsibilities

• Build GenAI, RAG, Agentic AI, and AI-powered applications.
• Develop Neo4j Knowledge Graph / GraphRAG solutions.
• Build data and AI pipelines using Databricks and PySpark.
• Develop scalable APIs, microservices, and backend apps in Python or Go.
• Prototype and deliver POCs/MVPs for customer needs.
• Deploy AI solutions across AWS, Azure, or GCP.
• Work with LLM frameworks, vector databases, Kubernetes, and cloud infra.
• Troubleshoot and optimize AI applications for performance and cost.
• Act as a technical consultant to enterprise customers.

Skills

Neo4j / Knowledge Graph

Generative AI / LLM / RAG

Databricks / Spark / PySpark

Python or Go

POC Development / Rapid Prototyping

Cloud: AWS / Azure / GCP

LLM frameworks / Vector DBs

Kubernetes / Cloud-native infra

Tools

Docker

Terraform

Kubernetes

Job description

Tiger Analytics seeks a Lead AI Engineer to drive end-to-end AI Engineering for the Luma platform, leading architecture, design, and delivery of enterprise-scale AI solutions. You will directly engage clients, rapid-build POCs/MVPs, and deploy GenAI, RAG, and Knowledge Graph solutions in production.

The role requires hands-on leadership, strong problem-solving, and the ability to operate in ambiguous environments across AWS, Azure, or GCP. Collaboration with customers and teams is essential.