MLOps Engineer

The Kuala Lumpur office is the technology powerhouse of MoneyLion. We pride ourselves on innovative initiatives and thrive in a fast paced and challenging environment. Join our multicultural team of visionaries and industry rebels in disrupting the traditional finance industry!

As an MLOps Engineer, you will help design, build, and operate the next generation of our machine learning platform and infrastructure, enabling data scientists and ML engineers to reliably take models from experimentation to production at scale. You will work closely with Data Scientists, Data Engineers, and AI/ML Engineering teams to streamline end-to-end ML workflows and improve system design and architecture for production-grade ML solutions.

- Design, build, and maintain ML infrastructure and tooling to support the full ML lifecycle (data preparation, training, evaluation, deployment, monitoring, and retraining).

- Develop and maintain CI/CD pipelines for ML models, including automated testing, validation, and safe rollout/rollback strategies.

- Implement robust model deployment patterns (batch, real-time, streaming) and ensure scalability, reliability, and low-latency performance in production environments.

- Build and operate monitoring and observability for ML systems (data drift, model performance, system health), and define alerting/incident response processes.

- Partner with Data Scientists and ML Engineers to productize models, including feature engineering pipelines, model packaging, and environment standardization.

- Collaborate with Data Engineering to integrate ML workloads into data platforms and pipelines, ensuring data quality, lineage, and governance.

- Drive best practices in MLOps, including versioning (data, model, code), experiment tracking, reproducibility, and documentation.

- Contribute to security, compliance, and cost optimization aspects of ML infrastructure (access control, secrets management, resource utilization).

- Provide technical guidance and support to cross-functional teams on ML platform usage, tools, and workflows.