Machine Learning Engineer, Model Productionization | Swiss RegTech | Zurich (Hybrid)

TMS Technology; Zürich; Vor Ort; CHF 170.000 - 210.000; Vollzeit

Zusammenfassung

TMS Technology seeks an engineer who sits between data science and production to operationalize real-time models in a Kubernetes-native environment. You will own the end-to-end deployment, monitoring, and scaling of production workloads.

Responsibilities include taking models from prototype to live production, building low-latency inference pipelines, and maintaining Spark data flows while collaborating with data scientists to ensure reliable, scalable real-time decisions.

Qualifikationen

• Experience shipping production systems in software engineering.
• Hands-on Spark experience at real pipeline scale.
• Direct experience turning ML model handoffs into live production deployments.
• Ability to translate vague stakeholder direction into a concrete plan and tasks.

Aufgaben

• Take models from the data science team from prototype to live production.
• Build and optimise real-time inference pipelines with strict latency constraints.
• Own Spark-based data engineering pipelines for training and inference.
• Deploy, run, and troubleshoot workloads on Kubernetes in production.
• Translate model direction into a technical plan and lead execution with a small team.
• Collaborate with data scientists to convert experimental code into production-grade software.
• Contribute to CI/CD and monitoring practices around model deployment.

Kenntnisse

Production systems

Spark

Kubernetes

CI/CD

Monitoring

Tools

Spark

Kubernetes

MLFlow

CI/CD tooling

Jobbeschreibung

A Swiss company with nearly four decades of engineering pedigree behind the software that fights financial crime inside 1,500+ banks across 80 countries is looking for the engineer who sits between its data science team and production. With the data scientists building new cutting edge, real-time models, what's needed is the person who takes those and makes them run, fast, reliably, and at scale, inside a Kubernetes-native production environment built for real-time decisioning.

This is not a modeling role or a data science hire. It's an engineer with real production software discipline who's comfortable being the one who makes someone else's model actually work under load, can pull together the resources needed, and be the glue.

Responsibilities, updated:

• Take models built by the data science team from prototype to live production, owning the engineering that gets them there.
• Build and optimise real-time inference pipelines where latency and throughput are the constraint, not just accuracy.
• Own Spark-based data engineering pipelines feeding model training and inference.
• Deploy, run, and troubleshoot workloads on Kubernetes in production.
• Translate model and product direction into a concrete technical plan for the dedicated engineering resources, and lead in execution day to day.
• Work directly with data scientists, translating experimental or research-grade code into tested, maintainable, production software.
• Contribute to the CI/CD and monitoring practices around model deployment.

Requirements, updated:

• A software engineering background first where you have shipped production systems
• Hands-on Spark experience at real pipeline scale, not project or PoC work.
• Direct experience taking ML models from a data science handoff into live production, with real exposure to the performance and latency trade-offs that involves.
• Comfortable taking ambiguous or incomplete direction from stakeholders and turning it into a clear technical plan and task breakdown for a small dedicated team, rather than waiting for fully-specified requirements.

Nice to have, unchanged:

• Exposure to fintech, financial services, or another regulated industry.
• Familiarity with model lifecycle tooling (MLFlow or equivalent) or serving frameworks.