AI-native Software Development Engineer

Job description

Let’s shape the future of finance - together

At Isabel, we believe that real innovation happens when people and ideas connect

That’s why we’re building more than technology, we’re building an ecosystem.

One where every voice counts, and where your work can truly make a difference.

We’re looking for curious minds and collaborative builders. People who believe success means growing together, learning from each other, and challenging the status quo.

If that sounds like you, keep reading.

We are looking for a Java Developer to join our Isabel 6 and Trust Services delivery department. The team plays a crucial role in developing and enhancing our IntelliTrust platform, our in‑house real‑time transaction monitoring system. This is a unique opportunity to work on high‑impact, mission‑critical systems where your code directly supports core banking operations and provides a trusted and secure platform to our customers. You'll be part of an initiative that's central to our 2026‑2027 strategic roadmap, with visible executive sponsorship and clear business value.

What You’ll Be Working On: The Transaction Monitoring Enhancement Initiative

You’ll contribute to building advanced payment processing and analysis capabilities including:

• Real‑time velocity detection: Monitoring transaction patterns to detect anomalies in frequency, amount, and behavior
• Behavioral profiling engine: Processing customer transaction profiles to identify statistical deviations and pattern changes
• AI/ML integration: Building data pipelines, feature engineering, and model integration for behavioral anomaly detection
• Time‑series analysis: Implementing dispatcher architectures and time‑series database integrations for high‑frequency transaction monitoring
• Rule engine enhancements: Developing flexible, configurable analysis rules for amount deviations, pattern matching, beneficiary intelligence, and more
• Event‑driven architectures: Building queue‑based systems for real‑time transaction processing
• Model deployment & monitoring: Implementing ML model serving infrastructure and monitoring model performance in production

What You’ll Do

Development

• Design, develop, and maintain Java‑based microservices for transaction monitoring
• Implement real‑time transaction processing capabilities with performance optimization
• Build and integrate AI/ML models into production transaction monitoring workflows
• Develop feature engineering pipelines to prepare transaction data for ML models
• Implement model serving infrastructure for real‑time prediction and scoring
• Build configurable rule engines that empower business analysts to create detection rules autonomously
• Integrate with AMQP‑based message queues and external APIs
• Write clean, maintainable, testable code following Clean Code principles
• Apply BDD/TDD practices for comprehensive test coverage
• Participate in code reviews, ensuring high‑quality standards across the team
• Contribute to technical design decisions and architecture discussions

DevOps & Deployment

• Implement and maintain GitLab CI/CD pipelines for automated testing and deployment
• Manage Kubernetes deployments and container orchestration
• Deploy and manage applications across Dev, Acceptance, and Production environments
• Monitor application and model performance, troubleshoot production issues
• Implement logging, monitoring, and alerting for transaction monitoring systems
• Collaborate with infrastructure team on containerization and scalability
• Ensure code quality and security using Sonar, Fortify, and Mend

Collaboration & Teamwork

• Work closely with business analysts and data scientists to understand monitoring requirements
• Collaborate with architects on solution design
• Partner with BI team on data availability and profiling requirements
• Participate actively in Agile ceremonies (daily standups, reviews, retrospectives)
• Share knowledge and mentor junior developers
• Communicate technical concepts to non‑technical stakeholders

Tech We Use

Primary language: Java

Messaging: AMQP‑based queuing systems

Architecture: Microservices/monolith, event‑driven, domain‑driven design, hexagonal architecture

Infrastructure: GitLab CI/CD, Kubernetes, OpenShift

Data: Time‑series databases, PostgreSQL, Oracle, ML data pipelines

Testing: BDD/TDD practices, Postman, SoapUI, Gatling, RestAssured

Quality & Security: Sonar, Mend

Methodology: Agile Kanban

Job Requirement