Senior Machine Learning Engineer

Accountabilities:

• 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:

• 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.

Benefits:

• Fully remote working environment.
• Full-time contractor position.
• Opportunity to work on production machine learning systems with measurable enterprise impact.
• Exposure to a broad range of ML applications, including recommendations, forecasting, propensity modeling, and document intelligence.
• Combination of product-focused engineering and hands-on customer delivery work.
• Opportunity to collaborate across product, engineering, implementation, solution engineering, IT, and customer teams.
• Direct involvement in responsible AI, MLOps, model deployment, and production monitoring practices.