AI Engineer – Agentic AI Sales Platform Development

### AI Engineer – Agentic AI Sales Platform Development We are looking for an experienced AI Engineer to help build an AI-first sales platform for our real estate marketplace. The goal is to develop intelligent, autonomous AI agents that can assist property sellers throughout the sales journey—from lead qualification and customer interactions to follow-ups and sales workflow automation. #### Responsibilities * Design and develop agentic AI workflows for real estate sales and customer engagement. * Build AI agents capable of lead qualification, prospect nurturing, sales assistance, and automated follow-ups. * Implement RAG (Retrieval-Augmented Generation) systems to provide accurate and context-aware responses. * Integrate AI agents with external tools, APIs, CRMs, messaging platforms, and internal systems. * Develop memory, tool-calling, and multi-agent orchestration capabilities. * Optimize agent performance, reliability, observability, and evaluation metrics. * Deploy and maintain production-ready AI solutions. #### Required Skills * Strong proficiency in Python. * Hands-on experience with LLMs and Generative AI applications. * Experience with agent frameworks such as LangGraph, CrewAI, AutoGen, LangChain, or similar. * Experience building RAG pipelines and working with vector databases (Pinecone, Weaviate, Qdrant, Chroma, etc.). * Experience with prompt engineering, model evaluation, and AI system testing. * Familiarity with API integrations, cloud platforms (AWS, Azure, or GCP), and modern deployment practices. * Understanding of MCP (Model Context Protocol), tool calling, and AI agent architecture is a strong plus. #### Project Details * Engagement Type: Part-time / Contract * Project Duration: 1–3 months * Potential for long-term collaboration based on project success #### When Applying, Please Include 1. A brief summary of your experience building AI agents, LLM applications, or automation platforms. 2. Links to relevant projects, GitHub repositories, demos, case studies, or portfolio work. 3. The technology stack used in those projects, including: * LLMs/Models * Agent frameworks * Vector databases * Cloud platforms * Deployment and monitoring tools 4. A short description of your specific contribution to each project. 5. Your estimated availability (hours/week) for this engagement.