Forward Deployed AI Engineer -Neo4j / Knowledge Graph
At a glance
Mid-level Solutions & Sales Engineering role at Tiger Analytics Inc.. Remote, United States · full-time.
Pay not stated
Growth Roles summary, based on the employer's posting.
What you'll do
- Lead architecture, design, and delivery for enterprise Agentic AI solutions on the Luma platform
- Build Neo4j Knowledge Graph and GraphRAG systems alongside GenAI, RAG, and agent applications
- Create Databricks and PySpark pipelines plus Python or Go APIs, microservices, and backend systems
- Turn customer needs into rapid POCs and MVPs, then move successful solutions into production
- Deploy and tune AI applications across cloud environments for scale, reliability, performance, and cost
What you bring
- Hands-on Neo4j and Knowledge Graph experience is mandatory for this role
- You bring experience with Generative AI, LLMs, RAG, and Agentic AI applications
- You can use Databricks, Spark, or PySpark and build applications with Python or Go
- You have delivered rapid prototypes and deployed AI solutions on AWS, Azure, or GCP
- You solve and debug difficult technical problems while working directly with enterprise customers
Who this fits
This role suits a hands-on technical leader who can own an AI engineering workstream and serve as the client’s primary technical contact. You’ll work directly with customers, handle unclear requirements, and take solutions from prototype through production. The position is remote within the United States and carries substantial individual responsibility.
From the employer
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.
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