Data Engineer (MLOps) Opportunity
We are seeking an experienced Data Engineer with strong MLOps expertise and machine learning modeling experience in the financial domain. This role offers the opportunity to work with state-of-the-art machine learning and cloud infrastructure in a fast-paced, growth-oriented environment.
Key Responsibilities:
* Design and Develop Scalable Data Pipelines: Build robust data pipelines using Python, Airflow, and PySpark to process large volumes of financial transaction data.
* Implement and Optimize MLOps Infrastructure: Automate the full machine learning lifecycle from development to production on AWS.
* Build Deployment Pipelines for ML Models: Use SageMaker and other AWS services to deploy machine learning models in production.
* Collaborate with Data Scientists and Stakeholders: Implement machine learning solutions for fraud detection, risk assessment, and financial forecasting.
* Ensure Data Quality, Reliability, and Security: Guarantee data quality, reliability, and security across all data engineering workloads.
* Optimize Data Architecture: Improve performance, scalability, and cost-efficiency of data architecture.
* Implement Monitoring and Alerting Systems: Ensure production ML models perform as expected.
Qualifications and Skills:
* 3-5 Years of Experience in Data Engineering: Focus on MLOps in production environments.
* Strong Proficiency in Python Programming: Knowledge of data processing frameworks like PySpark.
* Experience with Workflow Orchestration Tools: Airflow is preferred.
* AWS Stack Expertise: SageMaker, Lambda, S3, and other relevant services.
* Machine Learning Model Deployment and Monitoring: Knowledge of deployment and monitoring in production.
* Data Modeling and Database Systems: SQL and NoSQL database systems.
* Financial Services or Payment Processing Domain: Knowledge of this domain is highly desirable.
* Containerization and CI/CD Pipelines: Familiarity with Docker and CI/CD pipelines.
* Excellent Problem-Solving Skills: Ability to work in a fast-paced fintech environment.