Senior Machine Learning Engineer

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 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. What you bring: • 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 • 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 • You bring deep expertise in MLOps and platform engineering, including model registries, feature stores, CI/CD, infrastructure as code, experiment tracking, and automated validation • You have a strong architectural understanding of distributed systems, event-driven services, streaming data, and cloud-native ML platforms, with the ability to optimize for performance, scalability, reliability, and cost • You define robust evaluation and governance strategies, including offline benchmarking, online experimentation, hallucination analysis, model risk assessment, and responsible AI practices About You • You bring 7-9+ years of experience in software engineering and machine learning, with a track record of leading and delivering complex, production-scale AI systems • You combine strong product judgment with technical depth, translating ambiguous business problems into scalable, high-impact AI solutions • You thrive in complex, fast-moving environments and bring clarity, ownership, and strategic thinking to drive long-term platform success Where you belong: You will be part of a growing team of AI and industry experts dedicated to serving customers across the Kingdom of Saudi Arabia / United Arab Emirates. We are building a collaborative, high‑impact environment that brings together deep AI expertise, strong industry knowledge, and regional understanding to help customers innovate, transform, and lead in their markets. This team thrives on working together to turn complex business challenges into practical, scalable solutions. You will find an environment that values curiosity, ownership, and continuous learning, where new ideas are encouraged and real‑world impact matters. This is a place for people who enjoy building something new. You will thrive here if you are customer‑centric, motivated by meaningful outcomes, and comfortable navigating ambiguity in a fast‑evolving AI landscape. About the team We are a team of engineers passionate about building valuable, scalable AI frameworks that drive real business impact. We thrive on solving challenging problems, innovating with cutting-edge technologies, and collaborating closely to deliver AI systems that are reliable, high-performing, and ready for production. Our team values ownership, technical excellence, and creating solutions that stand the test of scale and complexity. #LI-LP1 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 leadersh

7-9+ years of experience in software engineering and machine learning; expert-level programming in Python with strong software engineering skills in Java or Go; deep knowledge of machine learning, deep learning, and optimization across structured data, NLP, search, ranking and recommendations; extensive experience designing and operating end-to-end ML systems from data ingestion to deployment and lifecycle management; hands-on experience with modern ML and LLM tooling (PyTorch, TensorFlow, scikit-learn) including fine-tuning, evaluation, orchestration, and model serving; practical experience building generative AI applications using embeddings, vector databases, RAG pipelines, agent workflows, prompt engineering, and guardrails; deep expertise in MLOps and platform engineering (model registries, feature stores, CI/CD, infrastructure as code, experiment tracking, automated validation); strong architectural understanding of distributed systems, event-driven services, streaming data and cloud-native ML platforms; ability to define robust evaluation and governance strategies including offline benchmarking, online experimentation, hallucination analysis, model risk assessment and responsible AI practices.

Lead the design and delivery of production-grade ML and generative AI systems at scale, owning architecture and implementation across data pipelines, feature stores, training workflows, model-serving infrastructure, online experimentation and observability. 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). Shape performance, reliability, latency and cost efficiency in live environments. Guide technical direction on model selection, distributed training and inference, GPU utilization, model compression, prompt and retrieval optimization, drift detection, retraining strategy and responsible AI controls. Mentor other engineers and transform best practices into reusable patterns and platform capabilities.