About the Role
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.
In this role, you’ll lead the design and delivery of production-grade ML and generative AI systems that solve complex product and business problems at scale. You’ll own architecture and implementation across data pipelines, feature stores, training workflows, model-serving infrastructure, online experimentation, and observability.
You’ll drive advanced use cases across deep learning, NLP, ranking, recommendation, forecasting, semantic retrieval, and LLM applications, including RAG, tool use, evaluation harnesses, and safety controls. Your work will directly shape performance, reliability, latency, and cost efficiency in live environments. You’ll guide technical direction on topics such as model selection, distributed training and inference, GPU utilization, model compression, prompt and retrieval optimization, drift detection, retraining strategy, and responsible AI controls. You’ll mentor other engineers and turn best practices into reusable patterns and platform capabilities.
You bring expert-level programming skills in Python, along with strong software engineering expertise in languages such as Java or Go, enabling you to build scalable, production-grade systems
Key Responsibilities
- You have deep knowledge of machine learning, deep learning, and optimization techniques across structured data, NLP, search, ranking, and recommendation problems
- You have extensive experience designing and operating end-to-end ML systems, from data ingestion and experimentation to deployment, observability, and lifecycle management
- You bring strong hands-on experience with modern ML and LLM tooling (e.g., PyTorch, TensorFlow, scikit-learn), including fine-tuning, evaluation, orchestration, and model serving
- You have practical experience building generative AI applications using embeddings, vector databases, RAG pipelines, agent workflows, prompt engineering, and guardrails
How to Apply
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