Senior Java Engineer – AI/ML Productionisation for Anti-Financial Crime Software

Senior Java Engineer – AI/ML Productionisation

A Swiss home grown specialist software company, that has been building compliance and risk technology for financial institutions for over three decades is seeking a new Software Engineer to bridge the gap.

Their platform sits at the heart of how banks detect financial crime, processing high volumes of events, in real time, where accuracy and reliability are not negotiable.

Over the past ten years, they have been quietly building one of the more sophisticated AI capabilities in the compliance technology space and are further working on bringing this to a globally available live production state.

They are now looking for a Senior Java Engineer to join the team responsible for making that AI work in production.

The Engineering Problem

The AI team builds models. Your job is to make them real.

That means taking model outputs from the data science team and engineering them into production-grade services; robust, observable, scalable, and integrated into a high-throughput event-driven platform built on Kafka.

The challenges are not purely ML. They are software engineering challenges that happen to involve ML:

• How do you serve a model reliably under load?
• What happens when inputs drift or predictions degrade?
• How do you build fallback logic that keeps the system running when something unexpected happens?
• How do you make a model's behaviour observable to the engineers who operate it?

These are the problems you will own.

What You Will Be Doing

• Building and maintaining Java services that wrap, serve, and integrate ML model outputs
• Designing the Kafka-based pipelines that feed real-time events into scoring and prediction workflows
• Working closely with data scientists to understand model requirements and translate them into reliable engineering
• Handling the production realities of ML systems: versioning, monitoring, latency, failure modes
• Contributing to the architecture of how the AI capability scales over time

What They Are Looking For

• Strong Java backend engineering
• Real experience with Kafka in a production environment, not just familiarity
• Python confidence; you will interact with Python-based model artefacts and tooling regularly
• Hands-on exposure to ML systems in production; you do not need to build models, but you need to understand what happens when they go live
• Engineers who think about reliability, observability, and failure handling, not just feature delivery

Nice to Have

• Apache Spark - particularly relevant if you have worked on batch feature engineering or training pipelines
• Experience with model serving frameworks or feature stores
• Background in fintech, regtech, or any domain where correctness at scale is a hard requirement

What Makes This Different

• You are not building demos or internal tooling, the models you put into production are used by banks to detect fraud and financial crime at scale
• The AI roadmap here is a decade in the making, not a 2023 pivot and there is real depth to work with
• You will sit at the boundary between data science and backend engineering
• Close-knit team, meaningful problems, and a codebase that has been built with quality in mind

The Setup

Zurich, Switzerland | Permanent | Hybrid — 2/3 days on-site, 2/3 days remote

If you have strong Java instincts, you have worked on systems where ML outputs become part of the product, and you care about what happens after the model is trained, this is worth a conversation.