What are the responsibilities and job description for the Engineer - AML Monitoring position at Angove Partners?
Engineer - AML Monitoring
Role Overview:
Angove Partners is partnering with a scaling AML monitoring and financial crime technology company building the systems that detect suspicious financial activity across global banking and payments networks. AML systems analyse financial transactions over time to detect patterns linked to money laundering, sanctions evasion, and other financial crime typologies across customer networks and longer behavioural timelines.
We’re looking to connect with Engineers across machine learning, data engineering, backend systems, graph engineering, and MLOps who are interested in building large-scale systems at the intersection of financial crime, analytics, and regulatory technology.
Responsibilities:
- Building transaction monitoring systems that process large-scale financial data (real-time and batch)
- Developing rule-based and ML-driven detection scenarios for financial crime typologies
- Designing anomaly detection models to identify suspicious behavioural patterns over time
- Building graph-based systems to detect networks of related accounts and entities
- Reducing false positive rates in alerting systems while maintaining regulatory coverage
- Supporting alert triage workflows and case management systems used by investigators
- Building data pipelines for ingesting and normalising transaction data at scale
- Deploying and monitoring ML models used in AML detection systems
- Supporting infrastructure for continuous model retraining and performance monitoring
- Languages: Python, Java, Scala, Go
- Data Engineering: Kafka, Spark, dbt, batch streaming architectures
- ML: time-series models, anomaly detection, XGBoost, PyTorch
- Graph: Neo4j, graph databases, network analysis, GNNs
- Cloud: AWS, GCP
- Orchestration: distributed data processing systems
- MLOps: model deployment, monitoring, retraining pipelines
About you:
We’re interested in connecting with Engineers with experience in:
- Backend / Platform Engineering (distributed systems, scalable APIs)
- Data Engineering (transaction pipelines, large-scale processing, streaming systems)
- Machine Learning / AI Engineering (anomaly detection, classification, time-series modelling)
- Graph / Network Engineering (entity resolution, network analytics, fraud rings)
- MLOps / Infrastructure (model deployment, monitoring, retraining systems)
- Working with production systems at scale
- Interest in financial crime, AML, fraud, or regulated data environments
- Ability to operate in fast-paced, high-growth environments
- Background in fintech, payments, or financial services is beneficial, but not essential
If you’re an Engineer working in data-heavy systems, machine learning, graph analytics, or large-scale financial infrastructure, we’d be interested in speaking with you for a confidential discussion.