Job Title: Senior AI Engineer
Location: Remote (New York City Office)
Employment Type: Contract OR Fulltime (Choose One)
About Opportunity
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
Senior AI Engineer | Confidential
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) for 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
time savings (~40%), alert auto-close rates, and SAR auto-draft rates
Senior AI Engineer | Confidential
Share learnings and reusable patterns across the broader engineering team and Aquila portfolio
What We're Looking For
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
