Job description

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.

Requirements

Must-haves

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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythongoawsazuregcpllmkubernetesdocker

Platform: workable
Effort: medium