AI Engineer

Role Overview: As an AI Engineer for this remote position, you will be responsible for working with Automated Valuation Models (AVM) and deploying machine learning models in real-world scenarios. Your expertise will be required in using LangChain for building LLM-powered applications, as well as hands-on experience with Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, and Jupyter from the Data Science Toolkit. Additionally, you will need a strong background in feature engineering and data intelligence extraction, along with the ability to handle structured, semi-structured, and unstructured data. Your role will also involve integrating models into APIs and production environments using Python-based frameworks. Key Responsibilities: - Experience with Automated Valuation Models (AVM) and real-world deployment of machine learning models - Proficient in using LangChain for building LLM-powered applications - Hands-on experience with Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, and Jupyter from the Data Science Toolkit - Strong background in feature engineering and data intelligence extraction - Comfortable with structured, semi-structured, and unstructured data - Experience integrating models into APIs and production environments using Python-based frameworks Qualifications Required: - 3-5 years of experience working with AI/ML models, LangChain, Data Science Toolkit, feature engineering, data handling, and production integration - Problem-solving acumen and the ability to work with different sets of data in multiple projects (Note: Additional details about the company were not specified in the job description.) Role Overview: As an AI Engineer for this remote position, you will be responsible for working with Automated Valuation Models (AVM) and deploying machine learning models in real-world scenarios. Your expertise will be required in using LangChain for building LLM-powered applications, as well as hands-on experience with Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, and Jupyter from the Data Science Toolkit. Additionally, you will need a strong background in feature engineering and data intelligence extraction, along with the ability to handle structured, semi-structured, and unstructured data. Your role will also involve integrating models into APIs and production environments using Python-based frameworks. Key Responsibilities: - Experience with Automated Valuation Models (AVM) and real-world deployment of machine learning models - Proficient in using LangChain for building LLM-powered applications - Hands-on experience with Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, and Jupyter from the Data Science Toolkit - Strong background in feature engineering and data intelligence extraction - Comfortable with structured, semi-structured, and unstructured data - Experience integrating models into APIs and production environments using Python-based frameworks Qualifications Required: - 3-5 years of experience working with AI/ML models, LangChain, Data Science Toolkit, feature engineering, data handling, and production integration - Problem-solving acumen and the ability to work with different sets of data in multiple projects (Note: Additional details about the company were not specified in the job description.)