Generative AI Engineer – RAG Architect (Onsite Phoenix)

Robotics Technologies; Phoenix (AZ); On-site; USD 92,000 - 131,000; Full time

Job summary

Robotics Technologies in Phoenix, AZ seeks a Generative AI Engineer to design, implement, and operate scalable GenAI systems built on RAG architectures. You will develop end-to-end ingestion, embeddings, and retrieval pipelines, and deploy GenAI solutions in cloud environments.

You will work with languages and frameworks like Python, LangChain, LlamaIndex, and various LLMs, focusing on performance, security, and cost efficiency while collaborating with cross-functional teams.

Qualifications

• Hands-on experience with Generative AI and RAG workflows.
• Proficiency in Python.
• Experience with LangChain and LlamaIndex.
• Familiarity with vector databases and embeddings.
• Knowledge of REST APIs and cloud deployment.

Responsibilities

• Design scalable GenAI solutions using RAG architectures.
• Develop AI-powered apps with LLMs like GPT, Claude, Llama, Gemini.
• Build end-to-end data ingestion, embeddings, indexing, retrieval, and response pipelines.
• Refine prompts to optimize model performance and cost.
• Integrate vector databases and semantic search into GenAI apps.
• Collaborate with cross-functional teams to translate business needs into AI solutions.
• Monitor model accuracy, relevance, latency, and cost efficiency.
• Apply security, governance, and Responsible AI best practices.
• Deploy and maintain GenAI solutions in cloud environments.

Skills

Generative AI

RAG frameworks

Python

LLMs

LangChain

LlamaIndex

Prompt engineering

AI model evaluation

Vector databases

Embeddings

Semantic search

Information retrieval

REST APIs

Microservices

Cloud platforms

Docker

Kubernetes

CI/CD

DevOps

Tools

LangChain

LlamaIndex

Docker

Kubernetes

CI/CD

Job description

Robotics Technologies in Phoenix, AZ seeks a Generative AI Engineer to design, implement, and operate scalable GenAI systems built on RAG architectures. You will develop end-to-end ingestion, embeddings, and retrieval pipelines, and deploy GenAI solutions in cloud environments.

You will work with languages and frameworks like Python, LangChain, LlamaIndex, and various LLMs, focusing on performance, security, and cost efficiency while collaborating with cross-functional teams.