Description

Experience Level: 6+ Years Enterprise Java Backend + AI/ML Integration
Position Overview
Charles Schwab is seeking a highly skilled Senior Java Backend Engineer with specialized expertise in Artificial Intelligence (AI) and Machine Learning (ML) systems integration. In this role, you will lead the modern evolution of our core Move Money platforms within Wealth Management. You will not only build highly secure, scalable, and resilient distributed microservices for fund movement (ACH, wires, checks, and internal transfers) but also design and orchestrate the AI infrastructure driving intelligent transaction workflows. Based out of our premier technology hubs in Austin or Westlake, TX, you will architect an AI-powered foundation that transforms transactional workflows, enhances real-time fraud mitigation, automates complex compliance auditing, and delivers hyper-personalized financial processing at enterprise scale.
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 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.
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 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 / GCP 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

Key Skills

Education

Any Graduate