Back-End Developer

As a Back-End Developer at our company, you will be responsible for developing an AI-driven prescriptive remediation model for SuperZoom, CBRE's data quality platform. Your primary focus will be on analyzing invalid records flagged by data quality rules and providing suggestions for corrected values based on historical patterns. It is crucial that the model you develop learns from past corrections to continuously enhance its future recommendations. The ideal candidate for this role should possess a solid background in machine learning, natural language processing (NLP), data quality, and backend development. Your key responsibilities will include developing a prescriptive remediation model to analyze and suggest corrections for bad records, implementing a feedback loop for continuous learning, building APIs and backend workflows for seamless integration, designing a data pipeline for real-time processing of flagged records, optimizing model performance for large-scale datasets, and collaborating effectively with data governance teams, data scientists, and front-end developers. Additionally, you will be expected to ensure the security, scalability, and performance of the system in handling sensitive data. To excel in this role, you should have at least 5 years of backend development experience with a focus on AI/ML-driven solutions. Proficiency in Python, including skills in Pandas, PySpark, and NumPy, is essential. Experience with machine learning libraries like Scikit-Learn, TensorFlow, or Hugging Face Transformers, along with a solid understanding of data quality, fuzzy matching, and NLP techniques for text correction, will be advantageous. Strong SQL skills and familiarity with databases such as PostgreSQL, Snowflake, or MS SQL Server are required, as well as expertise in building RESTful APIs and integrating ML models into production systems. Your problem-solving and analytical abilities will also be put to the test in handling diverse data quality issues effectively. Nice-to-have skills for this role include experience with vector databases (e.g., Pinecone, Weaviate) for similarity search, familiarity with LLMs and fine-tuning for data correction tasks, experience with Apache Airflow for workflow automation, and knowledge of reinforcement learning to enhance remediation accuracy over time. Your success in this role will be measured by the accuracy and relevance of suggestions provided for data quality issues in flagged records, improved model performance through iterative learning, seamless integration of the remediation model into SuperZoom, and on-time delivery of backend features in collaboration with the data governance team.,