Lead GenAI Engineer: Data, RAG & AI Agents

Siri InfoSolutions Inc; Phoenix (AZ); On-site; USD 140,000 - 190,000; Full time

Job summary

Siri InfoSolutions Inc in Phoenix, AZ seeks a Lead AI / GenAI Engineer focused on Python-driven data preparation, RAG architectures, and agentic AI deployment. The role emphasizes building end-to-end GenAI solutions, integrating LLMs, embeddings, and vector databases with enterprise data and APIs.

The candidate should have hands-on experience with LangChain, LangGraph, and LlamaIndex, and a track record of delivering production-ready GenAI applications in data-rich environments.

Qualifications

• Strong Python development experience.
• Experience preparing, cleaning, transforming, and structuring data for AI/ML and GenAI use cases.
• Hands-on experience building RAG (Retrieval-Augmented Generation) solutions.
• Strong experience with Agentic AI / AI Agents implementation.
• Experience with LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, etc.
• Strong understanding of embeddings, vector databases, semantic search, chunking, and retrieval.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex.
• Experience building production-ready GenAI applications.
• Experience integrating AI solutions with APIs, enterprise applications, and databases.
• Strong understanding of prompt engineering and LLM orchestration.

Skills

Python development

Data preparation

RAG solutions

Agentic AI

LLMs

Embeddings & vector DBs

semantic search

LangChain

LangGraph

LlamaIndex

GenAI apps

API integration

Prompt engineering

Data pipelines

Tools

LangChain

LangGraph

LlamaIndex

Job description

Siri InfoSolutions Inc in Phoenix, AZ seeks a Lead AI / GenAI Engineer focused on Python-driven data preparation, RAG architectures, and agentic AI deployment. The role emphasizes building end-to-end GenAI solutions, integrating LLMs, embeddings, and vector databases with enterprise data and APIs.

The candidate should have hands-on experience with LangChain, LangGraph, and LlamaIndex, and a track record of delivering production-ready GenAI applications in data-rich environments.