Senior Machine Learning Engineer - Applied AI (Budapest, HU, 1031)

SAP
Budapest
Workplace: HybridFull timeFunction: Data Science & Machine LearningSkills: ["Mentoring","Technical ownership","Rigorous evaluation"]

Design, train, and ship predictive ML models using real deal and operational data to provide Account Executives with deal/quote scoring, classification, forecasting, and anomaly detection insights. Build grounded AI features for quoting using LLM automation with guardrails aligned to deterministic pricing and configuration. Own model evaluation with offline and online metrics, then productionize via MLOps (CI/CD, versioning, monitoring, drift detection, retraining) while using SQL to generate training data and identify key pains and opportunities.

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FursaFursa
SAP
SAP
2 hours ago

Senior Machine Learning Engineer - Applied AI (Budapest, HU, 1031)

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Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 2 hours agoStatus: Live

Job Summary

Design, train, and ship predictive ML models using real deal and operational data to provide Account Executives with deal/quote scoring, classification, forecasting, and anomaly detection insights. Build grounded AI features for quoting using LLM automation with guardrails aligned to deterministic pricing and configuration. Own model evaluation with offline and online metrics, then productionize via MLOps (CI/CD, versioning, monitoring, drift detection, retraining) while using SQL to generate training data and identify key pains and opportunities.
Location: Budapest
Workplace: Hybrid
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Design, train, and ship predictive models over real deal data for forecasting, deal/quote scoring, classification, and anomaly detection.
  • •Build LLM-driven, grounded AI automation for the quoting workflow with validation/guardrails against deterministic pricing and configuration state.
  • •Own the evaluation approach for shipped models using offline metrics, online outcomes, regression suites, and faithfulness checks.
  • •Implement MLOps practices to keep models healthy in production, including CI/CD, versioning, monitoring, drift detection, and retraining pipelines.
  • •Use SQL over large noisy operational datasets to identify key pain points and opportunities and to create training data for the models.
Travel: Low travel

Key Requirements

  • •Build predictive ML models for deal and quoting workflows, including feature engineering, model selection, training, and optimization for large operational datasets.
  • •Develop grounded AI capabilities for quoting using LLM-driven automation, decision support, and guardrails aligned to deterministic pricing/configuration platform logic.
  • •Own rigorous model evaluation by defining success criteria and running offline metrics, online outcomes, regression suites, and faithfulness checks.
  • •Implement MLOps for the full model lifecycle, including CI/CD for models, model/data versioning, monitoring, drift detection, and retraining.
  • •Perform strong SQL and data wrangling on messy, large-scale quote/ticket/telemetry datasets to support training data and insights.
Experience:AIMachine learningLLMMLOpsData analytics
Skills:MentoringTechnical ownershipRigorous evaluation
Tech Stack:PythonPandasNumPyScikit-learnPyTorchTensorFlowLLMRAGSQLFeature engineeringHyperparameter tuningJenkinsGitHub ActionsElasticsearchKibanaCI/CDRESTODataSAP BTPModel monitoring

Company Brief

SAP
Global enterprise software company best known for ERP systems and business applications covering finance, supply chain, procurement, HR, analytics, and customer management for large organizations.
Industry: Enterprise Software
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: Walldorf, Germany
Founded: 1972
Glassdoor
Glassdoor: 4.1
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