Senior Machine Learning Engineer

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Machine Learning EngineerData

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Requirements Summary

Demonstrated expertise in applied machine learning and data science, including experience taking models into production and measuring their performance in real-world environments.

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Machine Learning EngineerData

This role focuses on building production-grade machine learning capabilities that create measurable value within enterprise order management.
You will work across prediction, ranking, recommendation, forecasting, and document intelligence use cases.
The position combines product development with hands-on customer delivery, giving you ownership from problem definition through deployment and monitoring.
You will work with large-scale enterprise data, including complex and imperfect datasets, and turn them into reliable signals and models.
You will collaborate closely with product, implementation, solution engineering, IT, and customer teams to ensure models perform effectively in real-world environments.
The role offers the opportunity to influence technical standards, responsible AI practices, and the reproducibility of machine learning systems.
It is well suited to an experienced ML engineer who enjoys both deep technical work and customer-facing problem solving.

Design, train, evaluate, and improve machine learning models for prediction, ranking, recommendation, churn and propensity scoring, demand forecasting, and related order management use cases.
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 clearly defined and measurable machine learning problems.
Support customer delivery engagements by profiling enterprise data, tuning and validating models, and collaborating with implementation and solution engineering teams to deliver trustworthy results.
Build reproducible Python-based training pipelines and experiment tracking so models and results can be reviewed, reproduced, and defended.
Profile, clean, and validate large-scale enterprise SAP and relational data while clearly identifying data limitations and their impact on modeling.
Package models and pipelines for production deployment and define appropriate monitoring for model drift, performance regression, and data quality.
Work with IT and platform teams to diagnose production issues, translating model behavior into operational implications and helping resolve problems effectively.
Help integrate model outputs into the product stack through clean, reliable, and well-documented service interfaces.
Apply responsible AI practices, including bias evaluation, explainability, and careful handling of data.
Document models, assumptions, limitations, behavior, and trade-offs for technical and non-technical stakeholders, including engineers, delivery teams, sales, and customers.
Review peers' work and contribute to stronger standards for modeling rigor, reproducibility, and engineering quality.

Requirements

~2 min read

Demonstrated expertise in applied machine learning and data science, including experience taking models into production and measuring their performance in real-world environments.
Strong Python skills and proficiency with modern machine learning tools and libraries such as PyTorch or TensorFlow, scikit-learn, pandas, and NumPy.
Strong statistical foundations, including experimental design, appropriate evaluation metrics, and the ability to distinguish meaningful results from statistical noise.
Strong SQL skills and experience working with large relational datasets.
Experience building reliable data and feature pipelines that operate on schedules and can handle complex or messy source systems.
Experience preparing machine learning solutions for handoff to operations or platform teams, including packaging, documentation, and runtime requirements.
Comfortable working directly with customers and delivery teams in environments where requirements and data conditions can vary significantly.
TypeScript or JavaScript proficiency sufficient to integrate machine learning capabilities with a broader product stack.
Strong written and verbal communication skills, with the ability to explain technical models and trade-offs clearly to both business and technical audiences.
Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related technical field, or equivalent practical experience.
Working knowledge of Kubernetes and containerized deployment is preferred.
Experience with MLOps tools such as MLflow, Kubeflow, Weights & Biases, Airflow, or Dagster is preferred.
Experience with LLMs and agentic workflows, including retrieval-augmented generation, fine-tuning, evaluation frameworks, or vector databases, is a plus.
Experience with production-scale recommender systems or time series forecasting is advantageous.
Exposure to SAP data structures such as SD and MM, or other enterprise ERP data models, is preferred.
Experience in customer-facing implementation, delivery, or professional services environments is a plus.
Experience with AWS, Azure, or GCP and SAP HANA Cloud ML libraries such as PAL/APL is advantageous.
A graduate degree in machine learning, statistics, or a closely related field is a plus.