Senior Machine Learning Engineer
Core
Building production-grade machine learning capabilities for enterprise order management, including prediction, ranking, recommendation, forecasting, and document intelligence.
Role type
Senior Machine Learning Engineer (Production & Customer Delivery)
Builds
Production ML models and pipelines for enterprise order management systems
Domain
Enterprise Software / Order Management / ERP
Deliverable
production ML models
Required skills
Python, PyTorch/TensorFlow, scikit-learn, pandas, NumPy, SQL, Feature Engineering, Model Deployment, MLOps, TypeScript/JavaScript, Data Profiling, Responsible AI (via careerplan.io/jobs/8007667d-daa8-4762-9f06-df005d2b2eba-senior-machine-learning-engineer-at-jobgether)
Preferred skills
Kubernetes, MLflow/Kubeflow/W&B/Airflow/Dagster, LLMs/Agentic Workflows, Recommender Systems, Time Series Forecasting, SAP Data Structures, Cloud Platforms (AWS/Azure/GCP), SAP HANA Cloud ML
Responsibilities
Design, train, evaluate, and improve ML models for prediction, ranking, recommendation, churn, propensity scoring, and demand forecasting. Contribute throughout the modeling lifecycle including problem framing, data preparation, feature engineering, training, evaluation, and retraining strategy. Partner with product management to translate roadmap priorities into measurable ML problems. Support customer delivery engagements by profiling enterprise data, tuning and validating models, and collaborating with implementation teams. Build reproducible Python-based training pipelines and experiment tracking. Profile, clean, and validate large-scale enterprise SAP and relational data. Package models and pipelines for production deployment and define monitoring for drift and performance regression. Work with IT and platform teams to diagnose production issues. Integrate model outputs into the product stack through service interfaces. Apply responsible AI practices including bias evaluation and explainability. Document models, assumptions, limitations, and trade-offs for technical and non-technical stakeholders. Review peers' work to contribute to stronger standards for modeling rigor and engineering quality.
