Role overview
R021903
Romania
Engineering
Regular
What you'll work on
- Design, build, and maintain robust ML pipelines for both batch and real-time inference using tools such as SageMaker Pipelines, EventBridge, mlflow, and feature stores.
- Collaborate with Data Scientists, Engineers, and stakeholders to implement, monitor, and optimize ML workflows—including model training, evaluation, drift detection, and deployment.
- Enhance and manage CI/CD pipelines for ML models, oversee model promotion and retraining workflows, and ensure seamless integration across different environments.
- Lead the end-to-end delivery of ML features from architecture and implementation to monitoring and iteration, while guiding and mentoring junior engineers and promoting best practices.
- Support operational excellence by participating in on-call rotations, production incident response, post-mortems, and contributing to the planning and scoping of ML initiatives.
What we're looking for
- 7+ years in the software industry, with at least 5 years focused on ML engineering, pipelines, or applied machine learning.
- Hands-on experience with AWS SageMaker Studio, Tecton, and proficiency in Python and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
- Experience with experiment tracking (MLflow), orchestrating ML workflows (SageMaker Pipelines, EventBridge), and building/monitoring both batch and real-time inference pipelines.
- Familiarity with data preparation and streaming tools (Glue, EMR, Flink) for feature pipelines.
- Solid understanding of CI/CD for ML (model deployment, endpoint configuration, validation) and strong cross-functional collaboration skills with Data Science, Risk, and Engineering teams.
Tags & focus areas
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