Forward Deployed Engineer

About the Role
Enterprise Suite is how the world''s fastest-growing mid-market and enterprise finance teams run their books, close their quarter, and put AI agents to work on real accounting operations.
Forward Deployed Engineers (FDEs) are the team that makes that real: embedded with our customers, shipping production automation, and turning "AI pilot" into "AI in the general ledger."
This isn''t a traditional sales engineering role, nor is it a traditional product engineering role. FDEs report into Engineering, work shoulder-to-shoulder with Sales and Account Management on the deals and renewals that matter most, and own the last mile between what Core Engineering ships and what a specific customer actually runs.
You''ll sit at the intersection of engineering, applied AI, and enterprise finance, and you''ll be judged on one thing: did the customer''s business measurably change because you were in the room?

Responsibilities
What you''ll do
Pre-sale: Build the proof that wins the deal
Get hands-on with prospects before there''s a contract: build a working proof-of-value directly in (or against a realistic replica of) the prospect''s own data and systems—real integrations and real output, not a scripted demo.
Partner with Account Executives from first technical touch: run discovery with the prospect''s finance and IT teams to understand their chart of accounts, systems, and process gaps well enough to build against them.
Lead the technical evaluation: architecture and security/compliance reviews, integration feasibility, and technical RFP responses with the prospect''s IT and controllership teams.
Build the technical narrative and ROI case alongside Sales, and present it directly to economic buyers—CFOs and Controllers—as part of the deal cycle. Your proof-of-value often gets the deal signed.
Flag technical or data-readiness risk early so Sales scopes and prices the deal accurately, ensuring nothing you built pre-sale becomes a surprise post-signature.

Post-sale: Architect and ship in customer-specific production
Architect and deploy customer-specific configurations of AI Agents that automate complex accounting processes — strengthening internal controls, closing books faster, and removing manual work from real customer environments.
Perform deep technical troubleshooting: review and edit Python, write SQL for integrations, and diagnose configuration issues across customer systems.
Serve as the functional and technical escalation point for issues involving integrations, data flows, and automation logic — including reviewing and optimizing SQL-based validations and API-driven workflows.
Maintain SLAs for response and resolution; document issues comprehensively and escalate to Engineering with complete context.
Post-sale: Own the customer relationship and drive expansion
Lead high-impact transformation engagements with enterprise finance teams—diagnosing operational bottlenecks and identifying where AI Agents create the most leverage.
Act as the primary technical point of contact across implementations, engaging directly with CFOs, Controllers, and accounting leaders.
Guide and train customer teams through workflow redesign, change management, and the value realization required to scale AI Agent adoption across their organization—and carry that same proof-of-value discipline into expansion and renewal conversations.
Travel to customer sites as needed (up to 70%) for pre-sale technical evaluation, hands-on deployment, training, and adoption workshops.

Ongoing: Shape the product and the playbook
Be the Voice of the Customer: surface technical feedback, integration requirements, and product gaps to Product and Core Engineering—as input to their roadmap, not a substitute for it.
Partner with Product Management on learnings from onsite discovery sessions to design workflows centered on AI Agent deployment and business outcomes.
Build the playbook: create implementation guides, expansion frameworks, and reusable assets so every deployment makes the next one faster.
Educate customers and internal teams on the strategic value of AI-enabled workflow transformation and best practices for building durable AI Agents.

 

Charles Schwab & Co., Inc.
Senior Java Backend & AI Engineer – Wealth Management (Move Money Platforms)
Location: Austin, TX or Westlake, TX (Hybrid – 3 days in office) Position Type: Full-Time, Permanent Department: Wealth Management Digital Platforms – Move Money Engineering 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 / 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