About This Role
We are seeking a highly skilled Data Engineer (MLOps) to join our dynamic team working on advanced financial technology projects.
This remote role offers the opportunity to work with state-of-the-art machine learning and cloud infrastructure in a fast-paced, growth-oriented environment.
Role Overview
We are looking for an experienced Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain. In this role, you will be responsible for building robust data pipelines and ML infrastructure to support our payment processing systems, fraud detection algorithms, and financial analytics solutions.
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 Communication: 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.
* Data Architecture Optimization: Optimize data architecture to improve performance, scalability, and cost-efficiency.
* Monitoring and Alerting: 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).
* Familiarity with containerization (Docker) and CI/CD pipelines.
* Excellent problem-solving skills and ability to work in a fast-paced fintech environment.