Machine Learning Engineer with Timeseries data experience

Qualifications

• Languages & Frameworks: Good understanding of AWS Framework, Python (Pandas, NumPy), PyTorch, TensorFlow, Scikit-learn, PySpark
• ML/DL Expertise: Strong grasp of time-series models (ARIMA, Prophet, Deep Learning), anomaly detection, and predictive analytics
• Data Handling: Experience with large datasets, feature engineering, and scalable data processing

Responsibilities

• Model Development: Design, build, train, and optimize ML/DL models for time-series forecasting, prediction, anomaly detection, and causal inference
• Data Pipelines: Create robust data pipelines for collection, preprocessing, feature engineering, and labeling of large-scale time-series data
• Scalable Systems: Architect and implement scalable AI/ML infrastructure and MLOps pipelines (CI/CD, monitoring) for production deployment
• Collaboration: Work with data engineers, software developers, and domain experts to integrate AI solutions
• Performance: Monitor, troubleshoot, and optimize model performance, ensuring robustness and real-world applicability

Full Description

ML Engineer with Timeseries data experience

Location: Atlanta, GA--Day1 onsite

Job Description

• Model Development: Design, build, train, and optimize ML/DL models for time-series forecasting, prediction, anomaly detection, and causal inference.

• Data Pipelines: Create robust data pipelines for collection, preprocessing, feature engineering, and labeling of large-scale time-series data.

• Scalable Systems: Architect and implement scalable AI/ML infrastructure and MLOps pipelines (CI/CD, monitoring) for production deployment.

• Collaboration: Work with data engineers, software developers, and domain experts to integrate AI solutions.

• Performance: Monitor, troubleshoot, and optimize model performance, ensuring robustness and real-world applicability.

• Languages & Frameworks: Good understanding of AWS Framework, Python (Pandas, NumPy), PyTorch, TensorFlow, Scikit-learn, PySpark.

• ML/DL Expertise: Strong grasp of time-series models (ARIMA, Prophet, Deep Learning), anomaly detection, and predictive analytics

• Data Handling: Experience with large datasets, feature engineering, and scalable data processing.