about 2 months ago
Responsibilities
- Act as a technical leader translating business fraud prevention goals into scalable technical solutions.
- Design and build large-scale, low-latency backend systems processing tens of millions of events per day.
- Develop and optimize real-time fraud detection systems using streaming data and inference pipelines.
- Collaborate across stakeholders including Product, Data Science, ML engineers, and leadership.
- Drive architectural decisions for scalable fraud detection.
- Evaluate and implement ML and emerging LLM-based approaches for fraud detection use cases.
- Advocate engineering excellence, including system efficiency, scalability, maintainability, and fault tolerance.
- Guide best practices and act as a role model for strong engineering standards across the team.
Requirements
- 10–12 years of backend engineering experience.
- Strong experience building large-scale, real-time distributed systems.
- Hands-on experience with data streaming technologies (e.g., Apache Flink or similar).
- Strong proficiency in Java-based backend development.
- Experience designing and building low-latency, high-throughput systems.
- Experience working with OLTP databases in production environments.
- Experience building or supporting real-time inference systems (ML or rule-based).
- Familiarity with using GenAI/LLM tools for engineering productivity.
- Strong knowledge of system design, scalability, and fault tolerance.
- Strong problem-solving skills with a focus on efficiency and optimal solutions.
Benefits
- Hybrid work model allowing flexibility to work from home 2 days a week.
- Collaborative culture that enriches the employee experience.