Key skills
Machine Learning InfrastructureSoftware EngineeringDistributed SystemsScalaPythonDistributed Processing FrameworksSparkFlinkOperating Systems at ScalePerformance TuningIncident ResponseAWSAIMachine LearningMLCommunication
About this role
Oscilar is building the next generation of AI-powered risk decisioning for fintech. They are seeking a Machine Learning Engineer to build, deploy, and maintain the ML infrastructure that powers their platform, working closely with data scientists and engineers to integrate ML capabilities.
Responsibilities:
- Scale and optimize existing ML systems. Improve the performance, reliability, and cost-efficiency of our current ML infrastructure, including feature stores, model serving, and orchestration pipelines
- Build reproducible, automated ML pipelines. Design and operate the pipelines that power model training, deployment, and monitoring across the platform — so models ship reliably and repeatably, not as one-off integrations. Partner with data scientists to make low-latency production deployment a paved path
- Build new ML infrastructure. Design and implement new components of our ML stack as the platform grows, with a focus on scalability, modularity, and developer experience
- Set ML engineering standards. Help define best practices for model deployment, monitoring, and lifecycle management. Mentor teammates and raise the bar across the organization
- Own production reliability. Be responsible for the uptime, performance, and correctness of ML systems serving real-time, business-critical decisions
Requirements:
- 4+ years of experience building and maintaining production ML infrastructure
- Strong software engineering fundamentals, with experience designing distributed systems and writing high-quality, maintainable code
- Hands-on experience with the full ML lifecycle in production: feature engineering and serving, model deployment, monitoring, and retraining
- Proficiency in Scala and Python, with hands-on experience building data and ML workloads on distributed processing frameworks such as Spark and Flink
- Experience operating systems at scale, including performance tuning, observability, and incident response
- Strong communication skills and the ability to collaborate effectively across data science, engineering, and product teams
- Significant experience building and operating workloads on AWS
- Experience building ML infrastructure for fintech applications
- Track record of scaling ML systems through significant growth in traffic, models, or feature volume