Senior Machine Learning Engineer - Applied AI

What you'll build

• Predictive models over real deal data. Design, train, and ship models that give Account Executives insight in-flow: forecasting, deal/quote scoring, classification, anomaly detection over large operational datasets. Feature engineering, model selection, training, and optimization are the core of the job, not a side task.
• Grounded AI features for the quoting workflow. LLM-driven automation, decision support, and guardrails that reconcile against the platform's deterministic pricing/config engine. RAG, grounding/faithfulness validation, tool-calling over authoritative platform state.
• Rigorous evaluation. Own the eval story for what you ship: offline metrics and online outcomes, regression suites over real quote records, faithfulness checks for the LLM features. You define what "good" means and prove it.
• MLOps and the model lifecycle. CI/CD for models, model/data versioning, monitoring, drift detection, and retraining, so models stay healthy in production, not just at ship time.
• Analytics that justify the work. SQL over millions of noisy quote/ticket/telemetry records to find where the real pains and opportunities are, and to build the training data behind the models.

Where you belong

You'll join the team building the systems SAP Account Executives rely on to quote, price, and close complex deals, and we're now bringing AI into those workflows. As a senior technical contributor you'll own the modeling work end-to-end, from training data to production model to the pipeline that keeps it healthy, and act as a technical reference point for less-experienced engineers and a peer to senior ones. Mentoring is a real expectation at this level, not an optional extra.

What you bring

• Core ML: feature engineering, model selection, training, hyperparameter tuning, and evaluation, across classical ML (regression, classification, forecasting, ranking) and LLM-based systems
• Python ML stack: pandas/NumPy, scikit-learn, PyTorch or TensorFlow, plus LLM / RAG frameworks
• Rigorous model evaluation: offline metrics, online outcomes, regression suites, faithfulness checks
• MLOps: CI/CD for ML, model and data versioning, monitoring, drift detection, retraining
• Strong SQL and data wrangling on large, messy operational datasets
• Observability tooling: Jenkins, GitHub Actions, Elasticsearch, Kibana
• Cloud-native deployment and REST/OData integration; SAP BTP a plus
• Domain exposure to CPQ, quoting, pricing, or sales/revenue processes a plus

What you get from us:

• Constant learning and skill growth in a wide range of technology areas
• A team that wants you to grow and succeed
• Flexible working models with a strong focus on health and well-being
• Recognition for individual contributions and a variety of benefit options to choose from

We help the world run better. At SAP, we enable you to bring out your best. Our company culture is focused on collaboration and a shared passion to help the world run better. We focus every day on building the foundation for tomorrow and creating a workplace that embraces differences, values flexibility, and is aligned to our purpose-driven and future-focused work.

SAP is the market leader in end-to-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with more than 200 million users and over 100,000 employees worldwide, we serve over 400,000 customers and touch 80% of global commerce across more than 20 industries.

SAP's culture of inclusion, focus on health and well-being, and flexible working models help ensure that everyone — regardless of background — feels included and can run at their best. SAP is an equal opportunity employer and is committed to creating a diverse environment. We believe in unleashing all talent and creating a better and more equitable world.