About the Company
Amiio is an AI-powered platform built for real estate investors and asset managers. It transforms fragmented financial, technical, and commercial data into clear dashboards and predictive insights—helping real estate professionals optimize performance, reduce costs, and make smarter, faster decisions.
Designed for strategic decision-making in real estate, Amiio uses advanced AI to make complex analysis and insights generation fast, intuitive, and fully automated.
By combining innovation with intelligence, Amiio is redefining how the industry leverages data and AI—setting a new standard for modern real estate management.
About the Role
We’re looking for a talented and hands-on AI Engineer to lead the development of intelligent systems powering our platform. The ideal candidate is highly proficient in Python, has deep experience working with LLM agents, and is skilled in building custom AI models from scratch and RAG architectures. Experience with conversational AI systems and with designing and orchestrating multi-agent workflows to ensure coherent, context-aware interactions.
You’ll take ownership of designing and implementing AI-driven features, working closely with our product and engineering teams to integrate them into real-world solutions.
Responsibilities
- Design and build LLM-powered features using LLM multi agents system
- Implement custom machine learning models tailored to product needs
- Work extensively with structured and unstructured data, including pipelines development, preprocessing, and transformation
- Research, evaluate, and implement state-of-the-art AI tools, models and frameworks
- Collaborate with developers to integrate AI components into the platform
- Monitor, evaluate, and optimize model performance for continuous improvement
Requirements
- Strong Python skills with 3+ years of experience in AI/ML projects
- Hands-on experience with LLM agents (e.g., multi-agent orchestration, tool-using agents, agent planning and reasoning, and context-managed conversational agents such as LangGraph, Hugging Face Transformers Agents, OpenAI)
- Proven ability to work with large datasets, including data cleaning, transformation, and optimization
- Deep understanding of Retrieval-Augmented Generation (RAG) and vector databases including embedding model selection and retrieval tuning
- Familiarity with LLM hosting, inference, performance tuning, and monitoring
- Experience building and integrating APIs (e.g., FastAPI)
- Self-driven, curious, and capable of independently solving complex problems
Nice to Have
- Familiarity with modern LLM fine-tuning techniques (PEFT framework) and RL-based optimization methods (e.g., LoRA/QLoRA, PPO, GRPO)
- Understanding of data privacy and security in AI applications
- Experience with cloud platforms (e.g., AWS, GCP, Azure)
- Experience building deployment pipelines for ML/LLM models using tools such as Docker, GitHub Actions, Kubernetes, and FastAPI