Production ML Systems Engineer

As a Machine Learning Engineer at Circuitry.Ai, your role will involve working on traditional ML algorithms and structured data systems. Your responsibilities will include structured data processing, feature engineering, model training & evaluation, deployment & automation. To excel in this role, you must have at least 2 years of hands-on ML experience, worked with datasets containing 2030M+ structured records, built and trained ensemble models, and have experience with algorithms such as XGBoost, Random Forest, Gradient Boosting, Logistic Regression, and SVM. Key Responsibilities: - Perform data sanity checks, clean data using SQL before moving to Pandas, and handle missing values, outliers, and transformations during data preparation. - Conduct deep Exploratory Data Analysis (EDA) to identify patterns, correlations, and distributions clearly. - Design structured feature engineering, derived features, aggregations, transformations, time-based features, and encoding strategies as part of feature engineering. - Train ensemble models and neural networks, run AutoML experiments, and compare and benchmark models effectively during model training. - Work with Accuracy, Precision/Recall, ROC-AUC, RMSE, and business KPIs, interpret confusion matrices, and explain results clearly to stakeholders during the evaluation process. - Deploy models into production, automate training and evaluation pipelines, and understand model behavior in production for deployment & automation. Qualifications Required: - 2+ years of hands-on ML experience - Worked with datasets containing 2030M+ structured records - Built and trained ensemble models - Experience with algorithms: XGBoost, Random Forest, Gradient Boosting, Logistic Regression, SVM If you enjoy working with structured data, are strong in feature engineering, think in terms of data pipelines, understand model trade-offs, and have deployed at least one production model, then this role at Circuitry.Ai is a perfect fit for you. Join us in building real production ML systems and taking ownership end-to-end! As a Machine Learning Engineer at Circuitry.Ai, your role will involve working on traditional ML algorithms and structured data systems. Your responsibilities will include structured data processing, feature engineering, model training & evaluation, deployment & automation. To excel in this role, you must have at least 2 years of hands-on ML experience, worked with datasets containing 2030M+ structured records, built and trained ensemble models, and have experience with algorithms such as XGBoost, Random Forest, Gradient Boosting, Logistic Regression, and SVM. Key Responsibilities: - Perform data sanity checks, clean data using SQL before moving to Pandas, and handle missing values, outliers, and transformations during data preparation. - Conduct deep Exploratory Data Analysis (EDA) to identify patterns, correlations, and distributions clearly. - Design structured feature engineering, derived features, aggregations, transformations, time-based features, and encoding strategies as part of feature engineering. - Train ensemble models and neural networks, run AutoML experiments, and compare and benchmark models effectively during model training. - Work with Accuracy, Precision/Recall, ROC-AUC, RMSE, and business KPIs, interpret confusion matrices, and explain results clearly to stakeholders during the evaluation process. - Deploy models into production, automate training and evaluation pipelines, and understand model behavior in production for deployment & automation. Qualifications Required: - 2+ years of hands-on ML experience - Worked with datasets containing 2030M+ structured records - Built and trained ensemble models - Experience with algorithms: XGBoost, Random Forest, Gradient Boosting, Logistic Regression, SVM If you enjoy working with structured data, are strong in feature engineering, think in terms of data pipelines, understand model trade-offs, and have deployed at least one production model, then this role at Circuitry.Ai is a perfect fit for you. Join us in building real production ML systems and taking ownership end-to-end!