AI & DATA ENGINEER

Location: Hồ Chí Minh, Việt Nam Division: Project Development Department Interview Process • Submit CV, Project Portfolio, and Cover Letter to [email]. • Initial interview with HR (15 minutes). • Round 1: Technical discussion with Product Manager (1-2 hours). • Round 2: Presentation of technology vision and strategy to CEO and senior leadership. • Final evaluation and offer discussion.

Data Pipeline Architecture: Design, build, and maintain scalable data pipelines to ingest, clean, and process massive volumes of real estate data from fragmented sources, including MLS feeds, public tax records, and real-time user clickstreams. Data Mining & Advanced Analytics: Apply pattern recognition, association analysis, and clustering algorithms to extract actionable intelligence from property data, identify market trends, and segment customer behaviors. Data Imputation & Quality Assurance: Build robust imputation models to automatically detect anomalies and estimate missing/erroneous data (e.g., missing bedroom counts or inaccurate square footage) from raw MLS feeds, ensuring high accuracy for downstream valuation algorithms. Data Modeling & Storage: Design optimal Data Lake/Data Warehouse architectures to effectively store and manage a hybrid of structured data (prices, geospatial coordinates) and unstructured data (3D tours, property images, and legal documents). Real-time Data Strategy: Implement data streaming architectures (utilizing tools like Apache Kafka or AWS Kinesis) to support real-time data processing, ensuring low-latency updates for AI inference and property listings. Data Governance & Compliance: Establish strict data quality standards, encryption, and access controls to ensure the infrastructure strictly complies with data privacy laws, FinCEN reporting rules, and fair housing regulations. AI & ML Development: Develop and deploy core PropTech machine learning models, including deep learning-based property valuation algorithms (AVMs) and Contextual Bandits for personalized user recommendations. Multi-Agent AI Integration: Architect a multi-agent AI system that acts as a central coordinator, routing user intents (via function and tool calling) across different domain-specific skills such as property search, financing, and tour scheduling. Agentic Coding Execution: Utilize advanced AI coding assistants and agentic IDEs to automate data transformation scripts, generate infrastructure-as-code, and accelerate the prototyping of machine learning models.

Experience Minimum 3 years of proven experience combining Data Engineering and Machine Learning/AI development. Experience developing PropTech products is a strong advantage. Knowledge and Skills Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related technical discipline. Strong programming proficiency in Python and SQL for complex data manipulation and model development. Hands-on experience building data pipelines using cloud-based big data technologies (AWS, GCP, or Azure) and streaming platforms. Practical experience in training and deploying machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn. Proven ability to utilize Agentic Coding tools and workflows (AI-assisted IDEs, multi-agent parallel workspaces) to delegate tasks to AI and speed up software delivery. Ability to analyze document business

requirements:, and create data flows and diagrams. Skills in feature prioritization, and project management. Proficiency with tools like Jira, Trello, Notion. Excellent communication, persuasion, and teamwork skills. Ability to work under pressure, multitask, and adapt in a fast-paced start-up environment. Preferred

Qualifications:Prior experience in the PropTech industry, specifically working with Multiple Listing Service (MLS) data, RETS/RESO Web APIs, or open-source AVM repositories. Familiarity with LLMs , RAG and building function-calling AI agents capable of executing real-world actions. Knowledge of high-cardinality geospatial data processing for location-based property analysis and valuation. Experience with multimodal AI and computer vision, particularly in analyzing property images, floor plans, and 3D home tours. Familiarity with real estate data compliance and security programs, including FTC Safeguards and algorithms that mitigate historical bias Familiarity with Agile/Scrum processes and MVP development. Fluency in English for working with international documentation or partners. Management responsibility: Make daily reports/weekly/monthly. Perform periodic and irregular work reports at the request of the Product Manager and the Board of Directors. Relationships : Internal: Closely associated with departments in the Company. External: Ability to build and develop good relationships. Problems: Directly deal with arising arising in the assigned work. Report and ask for timely direction of the Product Manager or The Board of Directors in case of arising is beyond the ability and authority to make personal decisions. Accountability: Sense of responsibility and ability to withstand high pressure at work. Good moral character. Honesty, work in compliance with good discipline. Professional working attitude. Make efforts to learn more new knowledge.

Authority: Salary: competitive Working time: 5 days/week (Monday to Friday) Performance Bonuses: Quarterly and annual bonuses based on KPIs Comprehensive Insurance: Premium health insurance for you and your family Leave Policy: 14 days of annual leave + public holidays as per regulations Professional Development: Annual budget for training, workshops, and certifications. Work Environment: Modern office in the heart of District 2, Ho Chi Minh City Work-Life Balance: Wellness programs for physical and mental health, team-building activities. Working conditions: A dynamic, professional, friendly working environment with many opportunities for learning and advancement. Receive training to improve

qualifications:according to the

requirements:of the job position. Performance indicators: Evaluation of the working process as well as work efficiency will be considered on a yearly basis.