Java Backend & AI Engineer

Flow Global Technology; Austin (TX); On-site; USD 130,000 - 190,000; Full time

Job summary

Flow Global Technology is seeking a Backend Engineer to design, build, and support high-throughput, fault-tolerant Java backend systems for asset movement and real-time transaction processing. You will architect AI platform orchestration, deploy backend infrastructure for operationalizing AI/ML models within transactional pipelines, including LLM integration and automated decision engines.

The role also involves modernizing legacy applications into cloud-native microservices, building resilient

Responsibilities

• Design, build, and support high-throughput Java backend systems for asset movement and real-time transaction processing.
• Architect and deploy AI platform orchestration to operationalize AI/ML models in the pipeline, including LLM integration.
• Integrate deep learning and predictive modeling into Move Money workflows to optimize liquidity and routing.
• Partner with data science and cybersecurity teams to inject AI-driven anomaly detection into payment streams.
• Migrate legacy transactional applications to cloud-native microservices with CI/CD patterns.
• Build resilient, asynchronous data pipelines to aggregate transactional metadata for model training.
• Act as the technical bridge between AI DS teams and core Financial Platform architects for production deployments.

Job description

• Backend Engineering: Design, build, and support high-throughput, fault-tolerant Java backend systems handling critical asset movement and real-time transaction processing.
• AI Platform Orchestration: Architect and deploy the backend infrastructure required to operationalize AI/ML models within the transactional pipeline, including LLM integration, intelligent agent routing, and automated decision engines.
• Predictive Transaction Workflows: Integrate deep learning and predictive modeling into Move Money operations to optimize liquidity predictions, dynamically route funds, and intelligently clear complex brokerage exceptions.
• Intelligent Security & Fraud Mitigation: Partner with data science and cybersecurity teams to inject AI-driven anomaly detection models directly into active payment streams, identifying and mitigating risk with sub-second latencies.
• System Modernization: Migrate legacy transactional applications to high-performance, cloud-native architectures utilizing microservices, event-driven designs, and automated CI/CD patterns.
• Data Pipeline & Engineering: Build resilient, asynchronous data streaming pipelines to aggregate high-fidelity transactional metadata, preparing and feeding data structures to train and evaluate AI models.
• Enterprise Collaboration: Act as the technical bridge between AI Data Science teams and core Financial Platform architects, ensuring secure, compliant, and performant production deployments