Über
Role Summary: Builds, trains and tunes machine learning models. Translates data science experiments into scalable, production-ready ML solutions. Key Responsibilities Translate data science prototypes into production-grade ML services and pipelines. Build training and inference code with reproducibility, versioning, and automated testing. Implement scalable model serving (online/offline), batching, and latency/throughput optimization. Integrate model lifecycle tooling (tracking, registry, deployment automation, monitoring). Collaborate with Data Engineering on feature pipelines and data contracts. Own production health: drift detection, performance regression, rollback strategies, and incident response. Required Qualification: 5+ years software engineering with 2+ years shipping ML models to production. Strong Python skills and experience with ML frameworks (TensorFlow/PyTorch). Experience with containers and orchestration (Docker/Kubernetes) and API development. Understanding of ML system design (data leakage, training-serving skew, drift). CI/CD and DevOps practices applied to ML workloads (MLOps). Nice to have: Experience with feature stores, model registries, and model monitoring stacks. GPU optimization and distributed training experience. Experience with responsible AI toolkits and compliance requirements.
Sprachkenntnisse
- English
Hinweis für Nutzer
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