Overview

End-to-End Model Development, Design, train, and deploy machine learning models using Python (scikit-learn, TensorFlow, PyTorch) and R for specialized statistical analysis. Data Engineering Pipeline Automation – Build robust ETL/ELT pipelines for structured/unstructured data.

Responsibilities

• Design, train, and deploy ML models (supervised/unsupervised learning, NLP, time-series forecasting) using Python and R
• Optimize models for computational efficiency via parallelization, quantization, and cloud-native architectures
• Build robust ETL/ELT pipelines for structured/unstructured data using SQL, Spark, and Airflow
• Implement feature engineering workflows and ensure data quality for large-scale datasets (1M+ records)
• Partner with product, risk, and engineering teams to operationalize models into business applications
• Mentor junior engineers and lead code reviews to maintain best practices in MLops workflows
• Develop monitoring frameworks for model drift, bias detection, and performance degradation
• Ensure compliance with regulatory standards and internal governance policies
• Stay ahead of industry trends (e.g., LLMs, generative AI) and prototype solutions using no-code platforms
• Publish internal white papers on novel methodologies and present findings to stakeholders

Qualifications

• Demonstrated experience in end-to-end model development using Python and/or R
• Experience with supervised/unsupervised learning, NLP techniques, time-series forecasting
• Strong knowledge of ETL/ELT pipelines and tools like SQL, Spark, Airflow
• Proficient in ML frameworks including scikit-learn, TensorFlow, PyTorch
• Knowledge of cloud-native environments such as AWS SageMaker, GCP Vertex AI
• Understanding of model governance practices, drift detection, bias detection, etc.

Nice to have

• Experience with generative AI technologies
• Proficiency in no-code or low-code platforms for ML prototyping
• Experience with internal knowledge sharing and presentation of research findings