Placy is an innovative platform leveraging the power of AI with the expertise of human agents to transform property transactions. Placy consists of two core elements:

1. Placy Free AI Assistant: Your companion on WhatsApp/Telegram for buying and selling real estate.
2. Placy Pro: Our white-label B2B tool for real estate professionals, property developers, and estate agencies, helping them scale up by automating half of agents’ work for a quarter of the usual costs.

Our mission? To revolutionize property deals with AI. Our vision? A world where property transactions are simple, transparent, efficient, personalized, and automated.

Ready to join us on this journey? 🚀

About The Team

At Placy, we are passionate about entrepreneurship and love our product too. Our Customer Success team is primarily charged with providing our business customers with a first-class experience and driving maximum value against their requirements. We aim to drive product adoption and utilization. We are a fast-growing start-up with more than 15 people within the first 6 months.

What You'll Work On:

• Architect and implement robust backend systems for our AI-driven Real Estate Assistant, ensuring seamless operation across diverse messaging platforms
• Engaging in the automation of processes to improve efficiency and reduce manual intervention
• Leveraging fundamental AI models, exploring opportunities for improvements, and navigating through various databases, including relational, document, and vector databases, according to project needs
• Develop solutions for high-load environments, focusing on scalability and system resilience

Our Tech Stack:

• Serverless architecture with Microsoft Azure
• OpenAI for foundational AI model integration
• Pinecone for vector database management (knowledge appreciated but not required)

What We're Looking For:

• Experience: Minimum of 5 years in backend development within high-load scenarios. Demonstrable Python expertise, preferably supported by recent code samples or GitHub repository links
• AI Knowledge: Practical AI experience, including R&D or personal projects. Familiarity with fine-tuning, RAG (Retrieval-Augmented Generation), and vector search principles preferred
• Architecture Understanding: Strong understanding of serverless and microservice architectures versus monoliths, with the ability to tackle high-load challenges. Foundational DevOps and DevSecOps knowledge required