Java Backend & AI Engineer / backend Engineer/

Core Responsibilities

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 architecture 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.

Technical Qualifications & Requirements

Core Backend Capabilities

• Deep mastery of Java (Java 11 / 17 or later) and enterprise ecosystem development.
• Advanced experience with Spring Boot, Spring Cloud, Spring Security, and Hibernate/JPA frameworks.
• Proven expertise in designing and scaling distributed systems, RESTful microservices, and high-volume transaction architectures.
• Robust understanding of event-driven software architectures using Apache Kafka or RabbitMQ.
• Strong relational database proficiency (Oracle, SQL Server) focusing on complex transactional consistency, ACID properties, and tuning.

AI / ML Integration Capabilities

• Extensive experience serving and integrating AI/ML models in Java runtimes utilizing tools like LangChain4j, ONNX Runtime, or Deep Java Library (DJL).
• Hands-on practice orchestrating interactions with Large Language Models (LLMs) via secure Enterprise APIs for text summarization, data extraction, or automated reasoning.
• Familiarity with Vector Databases (such as pgvector, Pinecone, or Milvus) to support Retrieval-Augmented Generation (RAG) within financial applications.
• Practical experience collaborating with Python-based ML engineering environments and operational frameworks (MLflow, Kubeflow) to transition model weights into high-performance Java APIs.
• Familiarity with AI guardrails, model alignment testing, and architectural implementations that minimize hallucination or biases in transactional routing.

Cloud, DevOps & Tooling

• Experience developing containerized deployments within enterprise cloud native infrastructure (Google Cloud Platform / Google Cloud Platform or Pivotal Cloud Foundry / PCF).
• Proficiency managing infrastructure deployments via Docker and Kubernetes environments.
• Expertise in continuous integration/delivery pipelines built using GitHub Actions, Bitbucket, or Bamboo.
• Rigorous standard for testing, adhering strictly to Test-Driven Development (TDD) or Behavior-Driven Development (BDD) paradigms with JUnit and Mockito.

Preferred Domain Experience

• Direct experience building Move Money systems (ACH clearing, domestic/international wire orchestration, internal journaling, or direct deposit networks).
• Strong foundation in financial compliance frameworks, audit trails, multi-factor risk checking, or anti-money laundering (AML) detection patterns.

Prior history navigating regulated spaces like brokerage platforms, retail banking ecosystems, or institutional wealth management applications