Senior Machine Learning Engineer - Applied AI

We help the world run better At SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed.     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.

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

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.    Bring out your best SAP innovations help more than four hundred thousand customers worldwide work together more efficiently and use business insight more effectively. Originally known for leadership in enterprise resource planning (ERP) software, SAP has evolved to become a 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 two hundred million users and more than one hundred thousand employees worldwide, we are purpose-driven and future-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best.   We win with inclusion SAP’s culture of inclusion, focus on health and well-being, and flexible working models help ensure