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A leading embedded financing platform is looking for an ML Ops Engineer to build and maintain robust machine learning infrastructure in a fast-paced production environment. This role is crucial in bridging the gap between research and deployment, ensuring AI-driven credit risk solutions are seamlessly integrated and continuously optimised.
Key Responsibilities- Design, implement, and optimise scalable ML pipelines for training, validation, deployment, and monitoring.
- Develop automation tools to streamline model updates and operational processes.
- Implement performance monitoring and alerting systems to track model accuracy.
- Collaborate with ML researchers and software engineers to integrate new models into production.
- Proven experience in building and maintaining operational ML systems.
- Strong Python coding skills with expertise in ML and data engineering libraries.
- Experience with CI/CD, containerisation (Docker, Kubernetes), and version control (Git).
- Strong problem-solving ability to identify and resolve issues in complex ML pipelines.
- Familiarity with cloud infrastructure and data tools such as AWS, Terraform, Snowflake, and Jenkins.
- Experience within financial services or lending environments.
This is an opportunity to join a forward-thinking organisation shaping the future of embedded finance.
