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# Machine Learning Engineer

- **Company:** SAP LTD.
- **Location:** Bellevue, WA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, SAP (Applications), Enterprise Software Applications, Pytorch, Large Language Models, Prompt Engineering, HuggingFace
- **Published:** June 27, 2026
- **Apply:** https://dejobs.org/x/x/9E63CAC96BE74243BF9A0DDA3915671D/job/

## About the Role

* 3 years of experience in ML engineering or data science delivering production solutions.
 * Strong foundation in ML and NLP.
 * Proficiency in Python, ML frameworks (e.g. PyTorch) and modern AI tooling (e.g. LangChain, Hugging Face)
 * Practical experience with LLMs, including fine-tuning, prompt engineering, RAG, and agentic architectures.
 * Solid understanding of evaluation methodologies.
 * Experience translating research into reliable production systems.
 * Ability to collaborate effectively across engineering, product, and business teams.
 * Curiosity and a continuous-learning mindset to keep pace with a fast-moving field.
 * Knowledge of privacy, security and compliance requirements for enterprise software.
 * Fluent in English.

## Description

As a ML Engineer in Bellevue, you will join a new team dedicated to building and maintaining customer support related AI and agentic solutions that collaborate with thousands of SAP support engineers worldwide and that interact with all of SAP's customers as part of SAP's mission critical and daily customer support offerings in preventive, reactive and value-adding scenarios across SAP's product and support portfolio. Your team will be part of the AI & Support Engineering organization with a decade of experience delivering enterprise AI at scale.

In your role, you will:

 * Design, train, and evaluate AI models that power customer support solutions.
 * Build and optimize RAG pipelines, embeddings, and agentic workflows for enterprise use.
 * Develop evaluation frameworks to measure model accuracy, relevance, and task completion.
 * Translate business problems into ML solutions with measurable outcomes.
 * Collaborate with team members to productionize applications.
 * Use prompt engineering, model orchestration, and tool-calling patterns to improve agents.
 * Stay current with the fast-evolving AI landscape.
 * Contribute to responsible AI and document model features, limitations, and design decisions for technical and non-technical stakeholders.


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