Forward Deployed AI Engineer -Neo4j / Knowledge Graph

Excerpt from the original posting

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.

- 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.

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