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Forward Deployment Engineer

Job in Columbia, Boone County, Missouri, 65201, USA
Listing for: Jobgether
Full Time position
Listed on 2026-10-30
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Data Engineering
Salary/Wage Range or Industry Benchmark: 135000 - 160000 USD Yearly USD 135000.00 160000.00 YEAR
Job Description & How to Apply Below

Our partner is looking for a Forward Deployment Engineer based in the United States.

The Forward Deployment Engineer will play a critical role in implementing AI-powered solutions that help financial institutions detect, investigate, and prevent financial crime. Combining technical expertise with customer-facing delivery, this position owns implementations from initial discovery through production launch. The successful candidate will design data integrations, configure fraud and anti-money laundering (AML) detection programs, and optimize AI agents that support investigation workflows.

Working directly with banks, credit unions, fintech companies, and cryptocurrency platforms, the engineer will translate complex business and regulatory requirements into reliable technical solutions. This role also involves managing multiple implementations simultaneously while adapting to evolving regulations, emerging fraud patterns, and advances in AI technology. It offers an opportunity to influence product development, build scalable implementation practices, and contribute to a collaborative, high-ownership environment focused on making financial systems safer.

Accountabilities
  • Own end-to-end implementations:
    Lead customer implementations from initial discussions through go-live, managing project plans, timelines, deliverables, and communication with business and technical stakeholders.
  • Design data integrations:
    Develop customer-specific integrations using real-time APIs, batch files, and core banking data extracts, including schema mapping, historical data backfills, validation, and production readiness.
  • Configure fraud and AML programs:
    Build, test, and optimize detection rules, screening processes, and customer risk rating (CRR) models to identify suspicious activity and financial crime risks.
  • Develop AI-powered investigation workflows:
    Configure and tune AI agents that help customer teams investigate alerts and cases, ensuring outputs and workflows support effective decision-making.
  • Implement regulatory workflows:
    Support customer requirements involving Suspicious Activity Reports (SARs), Currency Transaction Reports (CTRs), Customer Risk Rating (CRR), and other relevant compliance processes.
  • Test and troubleshoot technical solutions:
    Validate data quality, investigate integration issues, debug failures, and ensure implementations operate reliably in production.
  • Train and enable customers:
    Educate customer teams on the implemented platform and workflows, ensuring they can operate independently before transitioning to Customer Success.
  • Manage concurrent projects:
    Keep multiple implementations on schedule, respond to changing requirements, communicate risks early, and develop recovery plans when delivery timelines are threatened.
  • Develop repeatable implementation practices:
    Turn project learnings into reusable playbooks, standardized processes, and scalable implementation methodologies.
  • Collaborate with Product and Engineering:
    Share customer feedback, technical challenges, and recurring implementation patterns to inform product improvements and future development.
Requirements
  • At least 5 years of experience in implementation, solutions engineering, solutions architecture, technical consulting, or forward-deployed engineering for a B2B SaaS product.
  • Experience in fintech, regulatory technology (regtech), banking, or financial crime prevention is highly desirable.
  • Strong technical proficiency with REST APIs, Postman, SQL, relational databases, ETL pipelines, and at least one programming language, such as Python, JavaScript, or Type Script.
  • Ability to design, implement, and troubleshoot end-to-end data integrations, including data mapping, schema validation, historical backfills, and production data quality checks.
  • Strong data analysis skills, with the ability to interpret customer data structures, identify missing or inconsistent information, and communicate required changes to technical stakeholders.
  • Familiarity with core banking data and systems from providers such as FIS, Fiserv, or Jack Henry is an advantage.
  • Ability to translate fraud patterns and compliance requirements described by Bank Secrecy Act (BSA) officers or fraud leaders into detection rules and validate their effectiveness against real-world data.
  • Hands-on experience configuring or building large language model (LLM)-based workflows or AI agents, including writing instructions, evaluating outputs, testing behavior, and determining production readiness.
  • Understanding of fraud…
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