Wise

2 weeks ago

Senior ML Platform Engineer II - Financial Crime

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Build a greenfield model lifecycle platform that enables scalable, standardized development, deployment, evaluation, and monitoring of ML models for financial crime detection. Own the declarative training pipeline, model packaging/serving abstraction, evaluation framework, and monitoring with drift detection and audit trails. Work with data scientists, feature platform engineers, and Wise’s central ML platform team to integrate FinCrime lifecycle tooling on shared foundations.

Location: London

Workplace: Hybrid

Employment Type: Full time · Permanent

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Design and build a declarative, config-driven model training pipeline that data scientists can use without deployment code.
•Build model packaging and serving abstractions with a unified interface across multiple model types via a consistent API.
•Implement an evaluation framework with standardized metrics, reproducible comparisons, and automated validation gates.
•Develop model monitoring including drift detection, performance degradation alerts, automated retraining triggers, and audit trails for regulatory compliance.
•Own the integration layer with Wise’s central ML infrastructure so FinCrime lifecycle tooling aligns cleanly with shared foundations.

Pay and Benefits

Salary: GBP 111,000 - 145,000 annually

Equity and Bonus:Equity

Key Requirements

•Experience building ML platform infrastructure in production, including training pipelines, model serving, evaluation frameworks, or monitoring systems.
•Strong software engineering fundamentals, with experience using Python, Kotlin/Java, and SQL to build reliable, well-tested, maintainable systems.
•Experience with ML orchestration tools (Airflow, Kubeflow, or equivalent), model registries (MLflow or similar), and container-based deployment.
•End-to-end understanding of the ML lifecycle: data ingestion through training, packaging, serving, and monitoring.
•A product mindset for internal tooling—design for adoption by data scientists, not just functionality.

Experience:Financial crimeFraudAMLRegulated environments

Skills:Product mindsetBuilding for adoptionReliabilityMaintainabilityCollaboration

Languages:English

Tech Stack:PythonKotlinJavaSQLAirflowKubeflowMLflowContainersONNXAPI

Company Brief

Wise

Wise (formerly TransferWise) is a London-based fintech that provides low-cost international money transfers, multi-currency accounts and payment infrastructure for individuals and businesses worldwide.

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: London, United Kingdom

Founded: 2011

Glassdoor: 3.8

Website LinkedIn Glassdoor

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Wise

2 weeks ago

Senior ML Platform Engineer II - Financial Crime

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: London

Workplace: Hybrid

Employment Type: Full time · Permanent

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Design and build a declarative, config-driven model training pipeline that data scientists can use without deployment code.
•Build model packaging and serving abstractions with a unified interface across multiple model types via a consistent API.
•Implement an evaluation framework with standardized metrics, reproducible comparisons, and automated validation gates.
•Develop model monitoring including drift detection, performance degradation alerts, automated retraining triggers, and audit trails for regulatory compliance.
•Own the integration layer with Wise’s central ML infrastructure so FinCrime lifecycle tooling aligns cleanly with shared foundations.

Pay and Benefits

Salary: GBP 111,000 - 145,000 annually

Equity and Bonus:Equity

Key Requirements

•Experience building ML platform infrastructure in production, including training pipelines, model serving, evaluation frameworks, or monitoring systems.
•Strong software engineering fundamentals, with experience using Python, Kotlin/Java, and SQL to build reliable, well-tested, maintainable systems.
•Experience with ML orchestration tools (Airflow, Kubeflow, or equivalent), model registries (MLflow or similar), and container-based deployment.
•End-to-end understanding of the ML lifecycle: data ingestion through training, packaging, serving, and monitoring.
•A product mindset for internal tooling—design for adoption by data scientists, not just functionality.

Experience:Financial crimeFraudAMLRegulated environments

Skills:Product mindsetBuilding for adoptionReliabilityMaintainabilityCollaboration

Languages:English

Tech Stack:PythonKotlinJavaSQLAirflowKubeflowMLflowContainersONNXAPI

Company Brief

Wise

Industry: Payments

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: London, United Kingdom

Founded: 2011

Glassdoor: 3.8

Website LinkedIn Glassdoor