Jobgether is looking for a Machine Learning Engineer to work at the intersection of data science and engineering. You will build and scale high-performance machine learning systems that power global fraud detection, focusing on productionising models, designing pipelines, and ensuring real-time predictions at scale.

What You'll Do

Design, build, and manage scalable end-to-end ML pipelines, from raw data ingestion to model inference, optimized for terabyte-scale datasets.
Collaborate with data scientists to deploy performant, scalable, and maintainable machine learning models into production.
Implement workflow orchestration for multi-stage ML jobs using tools such as Prefect or similar frameworks.
Enhance and oversee MLOps infrastructure, including model versioning, automated deployments, monitoring, and observability.
Troubleshoot and resolve performance bottlenecks in production systems while ensuring system availability.
Continuously improve internal tools, engineering practices, and automation to streamline ML workflows.

What We're Looking For

Proven experience building and deploying machine learning models in production environments.
Strong understanding of the ML lifecycle, with hands-on experience designing training pipelines for large datasets.
Familiarity with workflow orchestration tools such as Prefect, Kubeflow, or Argo.
Solid grounding in software engineering fundamentals, including data structures, design patterns, Git, CI/CD, testing, and monitoring.
Excellent analytical and problem-solving skills, with the ability to work in ambiguous situations.
Strong communication and teamwork skills, with the ability to collaborate across technical and non-technical audiences.