Responsibilities
- Help design, build, and maintain the infrastructure that powers ML solutions
- Manage data pipelines, streamline model deployment, and optimize compute resources
- Work on high-impact systems such as ranking, recommendation, and pricing optimization
- Collaborate closely with data scientists to integrate models into production
- Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments
- Build and maintain efficient data pipelines for large-scale processing and feature engineering
- Optimize compute resources and improve model serving performance across ML systems
- Implement robust monitoring, logging, and alerting systems
- Contribute to ML Ops practices including CI/CD pipelines
- Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders
Requirements
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
- 6-7 years of experience in Software, Data, or ML engineering roles (preferred)
- Strong problem-solving skills
- Proficiency in Java and desirable Python
- Understanding of ML algorithms, model architectures, and experience building scalable, reliable ML systems
- Exposure to cloud platforms (e.g., AWS), containerization (Docker), and scalable data systems (e.g., Spark, Kafka)
- Familiarity with CI/CD tools (e.g., GitHub Actions), ML model serving technologies (e.g., MLflow)
- Ability to collaborate well across teams
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