
Forward Deployed AI Engineer -Neo4j / Knowledge Graph
Required skills
- Neo4j
- Generative AI
- LLM
- RAG
- Agentic AI
- Databricks
- Spark
- PySpark
- Python
- Go
Nice to have
- LangChain
- LlamaIndex
- LangGraph
- AutoGen
- GraphRAG
- Vector DBs
Role summary
Tiger Analytics seeks a Forward Deployed AI Engineer to build and deploy enterprise GenAI, RAG, Agentic AI, and Knowledge Graph solutions. This hands-on role involves rapid prototyping, developing POCs/MVPs, and production deployment across major cloud platforms (AWS, Azure, GCP). Key responsibilities include developing Neo4j Knowledge Graph/GraphRAG solutions, building data/AI pipelines with Databricks/PySpark, and creating scalable APIs/microservices using Python or Go. The engineer will act as a technical consultant, working directly with enterprise customers and optimizing AI applications for performance and scalability. Experience with LLM frameworks, vector databases, and Kubernetes is also valuable.
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.
- 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
LangChain, LlamaIndex, LangGraph, AutoGen, GraphRAG, Vector DBs, AWS Bedrock, Azure OpenAI, Kubernetes, Docker, Terraform, vLLM/Triton, PyTorch/Hugging Face.
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.
Sample Tiger Analytics Inc. interview questions
- 1
Design a search autocomplete system.
system designmedium - 2
Palindrome After Deleting One Character Determine if a string can be a palindrome after deleting at most one character. Input: s = "abc" Output: FALSE Explanation: Deleting any single character leaves either "ab", "bc", or "ac", none of which result in a valid palindrome.
codingmedium - 3
Valid Word Abbreviation Determine if a string matches a valid word abbreviation. Input: word = "apple", abbr = "a2e" Output: FALSE Explanation: The abbreviation claims exactly 2 characters are skipped between 'a' and 'e', but "ppl" is actually 3 characters long.
codingmedium
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