Senior AI Engineer

Building the next generation of AI-powered Anti-Money Laundering (AML) compliance

software. We are transforming a proven, production-grade AML platform - trusted by banks, credit

unions, and financial institutions - from a rules-based Decision Support System into an intelligent,

adaptive compliance ecosystem powered by Machine Learning, Natural Language Processing, and

agentic AI.

This is an opportunity to be the first AI hire at a high-performing, profitable fintech with deep domain

expertise and a clear roadmap. You will not be building demos. You will be designing and shipping

production AI that financial institutions rely on to detect financial crime.

You will work directly with AML compliance experts - the people who have spent careers investigating

suspicious activity, writing helped filing SARs, and interpreting regulatory guidance. Your job is to turn

their domain knowledge into requirements, and those requirements into working software. You will own

the full build cycle: from problem definition through architecture, implementation, testing, and piloting

with real clients.

What You’ll Do

Requirements Engineering

• Embed with AML compliance experts, investigators, and product leads to extracting domain

knowledge and translating it into structured technical requirements

• Run discovery sessions to understand investigative workflows, alert triage logic, SAR writing

processes, and analyst pain points

• Produce clear, testable specifications that bridge the gap between compliance expertise and

engineering execution

• Define acceptance criteria for AI model behavior in partnership with subject matter experts

Design & Architecture

• Design agentic AI workflows that automate alert enrichment, case creation, SAR drafting, and

investigative reasoning

• Architect multi-tool orchestration layers that integrate LLMs, ML models, and third-party platforms

(e.g., Google AML AI Enterprise, ComplyAdvantage, WorkFusion, Flagright, Symphony AI)

• Help define the AI-first software development lifecycle (SDLC), establishing

patterns for how AI capabilities are built, tested, versioned, and deployed

• Evaluate and select appropriate AI frameworks, models, and infrastructure in collaboration with

external consultants (AWS, Microsoft, Yonder)

• Research and evaluate AI technologies-LLMs, RAG, and assess their applicability to banking

workflows.

Build & Implementation

• Develop, train and deploy machinelearning and deeplearning models for use cases such as fraud

detection, AML/KYC automation and risk scores.

• Implement adaptive, ML-driven alerting that learns from analyst feedback to continuously reduce

false positives

• Write production-quality code to implement agentic AI workflows, ML model integrations, and NLP

pipelines on top of existing AML platform

• Develop natural language interfaces and LLM-powered workflows to enable compliance teams to

generate investigative insights and audit-ready reports with minimal manual effort.

• Build NLP and entity resolution capabilities to improve KYC screening, adverse media detection,

and counterparty mapping

• Build and maintain end-to-end ML pipelines while implementing MLOps best practices (CI/CD for

ML, model versioning, containerization, and automated retraining), covering the full lifecycle from

data ingestion and feature engineering to model training, evaluation, and production monitoring.

• Integrate AI models into production systems using APIs, microservices, or cloudnative

architectures.

• Integrate real-time risk scoring and dynamic entity enrichment from external data providers

Testing & Validation

• Document model behavior, assumptions, and validation results for internal audit and model risk

management.

• Design and execute model validation frameworks: precision, recall, false positive rate,

explainability scoring

• Build automated test suites covering AI pipeline behavior, edge cases, and regulatory

explainability requirements

• Perform A/B testing and backtestingback testing against historical alert and SAR datasets

• Ensure every AI decision produces a transparent, auditable record of reasoning that meets

regulatory standards

Piloting & Iteration

• Lead technical delivery of pilot deployments with early-adopter clients

• Collaborate closely with client compliance teams to gather feedback, measure model

performance against KPIs, and iterate rapidly

• Track and report against AI performance KPIs: false positive reduction targets (30-60%), analyst

Core Technical Skills

• Hands-on experience designing and shipping agentic AI systems (LLM orchestration, tool use,

multi-step reasoning agents) on existing AML enterprise Software bult with C# and ASP.NET

Core integrated with Microsoft SQL Server for transaction storage.

• Strong programming skills in Python (preferred), plus experience with Java/ASP.NET/C# as

needed.

• Handson experience with ML frameworks: PyTorch, TensorFlow, Scikitlearn or similar and NLP

libraries (spaCy, Hugging Face Transformers, or similar)

• Strong understanding of data security, encryption, and privacy best practices.

• Experience building and deploying models in cloud environments (AWS, Azure) and familiarity

with containerization.

• Bachelor’s or Master’s in Computer Science, Data Science, Engineering, Mathematics, or

related field.

Domain & Analytical Skills

• Ability to engage deeply with non-technical domain experts and extract structured requirements

from unstructured knowledge

• Experience translating ambiguous business problems into concrete engineering specifications

• Strong analytical skills for evaluating model performance, diagnosing failure modes, and

communicating results to both technical and non-technical audiences

• Familiarity with financial services compliance, AML, KYC, or fraud detection is a significant

advantage - but a demonstrated ability to learn complex regulated domains is equally valuable

Ways of Working

• Comfortable operating with autonomy in an early-stage AI buildout - you can scope work, make

architectural decisions, and drive delivery without waiting for a playbook

• Strong communicator: able to run discovery workshops with compliance experts, present

technical trade-offs to leadership, and write clear specifications

• Bias toward working software over documentation - you iterate in the real world and treat client

pilots as the best form of validation

• Collaborative by default: you share learnings, ask for input, and bring others along as the platform

evolves

Nice to Have

• Direct experience in AML, financial crime compliance

• Familiarity with SAR/CTR filing workflows or FinCEN regulatory requirements

• Experience with explainable AI (XAI) techniques relevant to regulated environments

• Prior work at a fintech, compliance software vendor, or financial institution