Forward Deployed AI Engineer -Neo4j / Knowledge Graph

Tiger Analytics; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Tiger Analytics is seeking a high-impact Lead AI Engineer to steer end-to-end AI engineering workstreams for the Luma platform. This role combines hands-on coding with architectural leadership, delivering GenAI, RAG, and Knowledge Graph solutions at enterprise scale.

You will work directly with customers, rapidly prototype POCs, and guide production deployments on AWS, Azure, or GCP, while shaping the technical direction of client engagements.

Qualifications

• Experience building GenAI, RAG, agentic AI solutions.
• Expertise with Neo4j Knowledge Graph technology is mandatory.
• Strong data engineering and AI application skills in Python or Go.

Responsibilities

• Lead end-to-end AI engineering workstreams for enterprise-scale solutions.
• Architect, design and deliver GenAI and knowledge graph deployments for clients.

Skills

GenAI / LLM / RAG / Agentic AI

Neo4j Knowledge Graph

Databricks / Spark / PySpark

Python or Go

Rapid Prototyping / POC

Cloud: AWS / Azure / GCP

Problem solving

Customer-facing

Tools

Neo4j

GraphRAG

Kubernetes

Docker

Job description

Good to Have

LangChain, LlamaIndex, LangGraph, AutoGen, GraphRAG, Vector DBs, AWS Bedrock, Azure OpenAI, Kubernetes, Docker, Terraform, vLLM/Triton, PyTorch/Hugging Face.

Tiger Analytics is seeking a highly experienced Lead AI Engineer to lead the end-to-end AI Engineering workstream for the Luma platform. This is a hands‑on technical leadership role responsible for driving the architecture, design, and delivery of enterprise‑scale Agentic AI solutions while serving as the primary technical interface for the client.

We are looking for a Forward Deployed AI Engineer to build and deploy enterprise GenAI, RAG, Agentic AI, and Knowledge Graph solutions. The role involves working directly with customers, rapidly developing POCs/MVPs, and taking solutions into production.

Requirements

• Build GenAI, RAG, Agentic AI, and AI‑powered applications
• Develop Neo4j Knowledge Graph / GraphRAG solutions - must have
• Build data and AI pipelines using Databricks and PySpark
• Develop scalable APIs, microservices, and backend applications using Python or Go
• Rapidly prototype and deliver POCs/MVPs for customer requirements
• Deploy AI solutions across AWS, Azure, or GCP
• Work with LLM frameworks, vector databases, Kubernetes, and cloud‑native AI infrastructure
• Troubleshoot and optimize AI applications for performance, scalability, reliability, and cost
• Act as a technical consultant and work closely with enterprise customers

Must‑Have Skills

• 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 working in ambiguous environments

Good to Have

LangChain, LlamaIndex, LangGraph, AutoGen, GraphRAG, Vector DBs, AWS Bedrock, Azure OpenAI, Kubernetes, Docker, Terraform, vLLM/Triton, PyTorch/Hugging Face.

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast‑growing, challenging and 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.