About the Role
We are seeking an experienced Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain.
Key Responsibilities:
* Data Pipeline Development: Design, develop, and maintain scalable data pipelines using Python, Airflow, and PySpark to process large volumes of financial transaction data.
* MLOps Infrastructure: Implement and optimize MLOps infrastructure on AWS to automate the full machine learning lifecycle from development to production.
* ML Model Deployment: Build and maintain deployment pipelines for ML models using SageMaker and other AWS services.
* Collaboration and Stakeholder Management: Collaborate with data scientists and business stakeholders to implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
* Data Quality and Security: Ensure data quality, reliability, and security across all data engineering workloads.
* Optimization and Monitoring: Optimize data architecture to improve performance, scalability, and cost-efficiency. Implement monitoring and alerting systems to ensure production ML models perform as expected.
Qualifications & Skills:
* 3-5 years of experience in Data Engineering with a focus on MLOps in production environments.
* Strong proficiency in Python programming and data processing frameworks (PySpark).
* Experience with workflow orchestration tools, particularly Airflow.
* Hands-on experience with AWS stack, especially SageMaker, Lambda, S3, and other relevant services.
* Working knowledge of machine learning model deployment and monitoring in production.
* Experience with data modeling and database systems (SQL and NoSQL).
* Knowledge of financial services or payment processing domain is highly desirable.
* Familiarity with containerization (Docker) and CI/CD pipelines.
* Excellent problem-solving skills and ability to work in a fast-paced fintech environment.