AI/Machine Learning Engineer

Job Description

Sigil Partners is seeking an experienced AI/Machine Learning Engineer to design, develop, and deploy intelligent solutions for real estate applications. This individual will collaborate with engineering, product, data, and business stakeholders to build scalable machine-learning systems that improve property analytics, valuation, forecasting, customer experiences, and operational decision-making.

The ideal candidate will bring strong machine-learning and software-engineering fundamentals, experience delivering production-grade AI solutions, and the ability to manage the complete model lifecycle—from data preparation and experimentation through deployment, monitoring, and continuous improvement. Experience with generative AI, large language models, geospatial data, or real estate technology is highly desirable.

Responsibilities

• Design, develop, train, evaluate, and deploy machine-learning and AI models for real estate applications
• Build predictive solutions for property valuation, market forecasting, customer behavior, recommendations, risk analysis, and operational optimization
• Develop generative AI and natural-language processing capabilities for property documents, contracts, listings, customer interactions, and internal workflows
• Create scalable data pipelines, feature-engineering processes, model APIs, and inference services
• Collaborate with data engineers, software engineers, product managers, analysts, and real estate subject-matter experts
• Translate business requirements and real estate use cases into measurable machine-learning solutions
• Conduct model experimentation, validation, benchmarking, and performance optimization
• Deploy and maintain models using modern cloud and MLOps platforms
• Monitor model accuracy, drift, latency, reliability, fairness, and production performance
• Write clean, maintainable, well-tested Python and production software
• Document datasets, experiments, model behavior, technical decisions, and operational procedures
• Apply responsible AI, data privacy, security, explainability, and governance practices
• Evaluate emerging AI technologies and incorporate them where they provide measurable business value

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, Engineering, or a related quantitative discipline
• 3+ years of professional experience developing machine-learning, data-science, or AI solutions
• Strong programming experience with Python
• Experience with machine-learning frameworks such as PyTorch, TensorFlow, scikit-learn, XGBoost, or similar technologies
• Strong knowledge of supervised and unsupervised learning, feature engineering, model evaluation, and statistical analysis
• Experience developing production APIs, services, or applications that incorporate machine-learning models
• Proficiency with SQL and experience working with structured and unstructured datasets
• Hands-on experience with AWS, Azure, or Google Cloud
• Familiarity with Docker, CI/CD, automated testing, version control, and modern software-development practices
• Experience deploying, monitoring, and maintaining models in production environments
• Strong analytical, problem-solving, communication, and cross-functional collaboration skills

Preferred Qualifications

• Experience developing generative AI applications or integrating large language models
• Familiarity with retrieval-augmented generation, embeddings, vector databases, prompt evaluation, and AI agents
• Experience with MLOps technologies such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure Machine Learning, or similar platforms
• Knowledge of real estate data, property valuation, automated valuation models, mortgage analytics, or property-management systems
• Experience with geospatial data, geographic information systems, mapping, or location-based modeling
• Familiarity with computer vision for property images, inspections, construction progress, or asset-condition analysis
• Experience with time-series forecasting, recommendation systems, optimization, or anomaly detection
• Understanding of fair housing considerations, model bias, explainability, data governance, and responsible AI
• Master’s degree in a relevant quantitative or technical discipline