AI Engineer

About Luupli

Luupli is a social media app that has equity diversity and equality at its heart. We believe that social media can be a force for good and we are committed to creating a platform that maximizes the value that creators and businesses can gain from it while making a positive impact on society and the planet. Our app is currently in Beta Test and we are excited about the possibilities it presents. Our team is made up of passionate and dedicated individuals who are committed to making Luupli a success.

Job Description

As an AI Engineer at Luupli you will play a pivotal role in developing intelligent systems and orchestrating agentic workflows that power Luuplis AI features. Your work will span Retrieval-Augmented Generation (RAG) multi-agent LLM orchestration auto-captioning generative media and content moderation.

Youll use frameworks like LangGraph LangChain and Googles Agent Development Kit to build persistent scalable AI services on Google Cloud Platform (GCP). This is a full-stack AI role that spans intelligent backend APIs LLM agent orchestration and integration with product-facing features.

Responsibilities

• Build and deploy multi-agent AI workflows using LangGraph LangChain or Googles Agent Development Kit.
  
• Implement RAG pipelines using embeddings semantic chunking and vector databases (e.g. FAISS Pinecone Weaviate).
  
• Integrate hosted and open-source LLMs (OpenAI Gemini Claude Ollama Mistral) into intelligent systems.
  
• Build REST APIs with FastAPI and internal tools with Streamlit to expose AI functionality.
  
• Deploy production-grade services on GCP using Vertex AI Cloud Run Cloud Functions IAM and Pub/Sub.
  
• Embed AI into platform features such as auto-captioning LuupForge (generative studio) feed personalization and real-time moderation.
  
• Maintain modular testable observable and secure code across the AI system lifecycle.
  

Requirements

• 3 years experience in applied AI/ML engineering (production-level deployments not research-only).
  
• Strong Python development skills with full-stack AI engineering experience:
  
  
  • FastAPI Streamlit
    
  • LangGraph LangChain or similar
    
  • PyTorch Transformers
    
  • FAISS Weaviate or Pinecone
    
• Solid experience working with hosted APIs (OpenAI Gemini) and self-hosted models (Mistral Ollama LLaMA).
  
• Deep understanding of LLM orchestration agent tool-use memory sharing and prompt engineering.
  
• Hands-on experience with Google Cloud Platform (GCP); especially Vertex AI Cloud Functions Cloud Run and Pub/Sub.
  
• Familiarity with best practices in cloud-based software development: containerization CI/CD testing monitoring.
  

Nice to Have

• Experience with Googles Agent Development Kit or similar agent ecosystems.
  
• Familiarity with multimodal AI (e.g. handling text image audio or video content).
  
• Prior experience developing creator platforms content recommendation engines or social media analytics.
  
• Understanding of ethical AI principles data privacy and bias mitigation.
  
• Experience with observability tools (e.g. Sentry OpenTelemetry Datadog).
  
• Data engineering experience such as:
  
  
  • Building ETL/ELT pipelines
    
  • Working with event-based ingestion and structured logs (e.g. user sessions reactions feeds)
    
  • Using tools like BigQuery Airflow or dbt
    
  • Designing or consuming feature stores for AI/ML applications
    

Compensation

This is an equity-only position offering a unique opportunity to gain a stake in a rapidly growing company and contribute directly to its success.

To Apply

Please send your resume and cover letter to with the subject line: AI Engineer

As part of your cover letter please respond to the following questions:

1. This position is structured on an equity-only basis. Thus it is presently unpaid until we secure seed funding. Given this structure are you comfortable continuing with your application for this role
   
2. Have you built or contributed to agent-based AI systems using frameworks like LangGraph LangChain or Googles Agent Development Kit
   
3. Do you have experience with Retrieval-Augmented Generation (RAG) systems and vector databases (e.g. FAISS Pinecone Weaviate)
   
4. Have you deployed AI systems on Google Cloud Platform If not which cloud platforms have you used and how
   
5. Have you integrated LLMs (e.g. OpenAI Gemini Claude) into autonomous or multi-step workflows
   
6. Can you explain how agents collaborate and maintain memory across tasked in multi-agent systems
   
7. What is your experience with prompt engineering tool invocation and orchestrated LLM workflows
   

Do you have any public code repositories (e.g. GitHub) demo URLs or project write-ups showcasing your work Please include links.

• Degree in Computer Science Engineering or related field
• Experience with machine learning deep learning NLP and computer vision
• Proficiency in Python Java and R
• Strong knowledge of AI frameworks such as TensorFlow or PyTorch
• Excellent problem-solving skills and ability to work in a team environment