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 build and operationalize machine learning and AI capabilities that from idea to production and deliver measurable product value. You'll develop data move pipelines, training and inference workflows, model-serving services, APIs, and evaluation frameworks for use cases such as recommendation, forecasting, anomaly detection, classification, NLP, semantic search, and generative AI. Your work will include feature engineering, experiment design, model tuning, offline and online validation, and integration of models into scalable product architectures. You'll help improve LLM-based workflows, including prompt design, retrieval-augmented generation, vector search, guardrails, and response quality evaluation. You’ll also strengthen the engineering backbone around AI through CI/CD, monitoring, observability, testing, and model lifecycle automation so solutions are reliable, cost-aware, secure, and ready for enterprise scale. What you bring: • You bring strong programming skills in Python, along with working knowledge of Java or Go, for building production-grade services and APIs • You have a solid understanding of machine learning fundamentals, including supervised and unsupervised methods such as classification, regression, clustering, ranking, and recommendation systems • You have hands-on experience with deep learning frameworks (e.g., PyTorch or TensorFlow) for model training, fine-tuning, and inference • You demonstrate strong capabilities in data preparation, feature engineering, data validation, and model evaluation using appropriate offline and online metrics • You have experience building, deploying, and integrating ML models into production systems through batch, real-time, or streaming pipelines • You are familiar with generative AI concepts, including LLMs, embeddings, vector databases, prompt engineering, and retrieval-augmented generation, and how to apply them in practical use cases • You bring working knowledge of MLOps and modern data infrastructure, including experiment tracking, model versioning, CI/CD, and tools such as Spark, Kafka, Airflow, and feature stores • You have experience operating ML systems in production, including monitoring for drift, latency, accuracy, cost, bias, and performing debugging and failure analysis to ensure reliability and business impact About You • You have 1-3+ years of experience in machine learning engineering, software engineering, or a related field, with a track record of deploying models into production • You are passionate about building reliable, scalable ML systems and take ownership of delivering end-to-end solutions • You balance experimentation with engineering rigor, making thoughtful trade-offs to ensure models are both innovative and production-ready • You are a collaborative problem-solver who thrives in ambiguous environments and is motivated by delivering measurable business impact 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. 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 (ER

What you bring: • Strong programming skills in Python, with working knowledge of Java or Go for building production-grade services and APIs • Solid understanding of machine learning fundamentals (supervised/unsupervised methods such as classification, regression, clustering, ranking, recommendation) • Hands-on experience with deep learning frameworks (PyTorch or TensorFlow) for training, fine-tuning, and inference • Data preparation, feature engineering, data validation, and model evaluation using offline and online metrics • Experience building, deploying, and integrating ML models into production systems through batch, real-time, or streaming pipelines • Familiarity with generative AI concepts (LLMs, embeddings, vector databases, prompt engineering, retrieval-augmented generation) • Working knowledge of MLOps and modern data infrastructure (experiment tracking, model versioning, CI/CD; tools like Spark, Kafka, Airflow, feature stores) • Experience operating ML systems in production with monitoring for drift, latency, accuracy, cost, bias; debugging and failure analysis for reliability

What you’ll build: You’ll develop and operationalize machine learning and AI capabilities from idea to production, including data pipelines, training and inference workflows, model-serving services, APIs, and evaluation frameworks for use cases such as recommendation, forecasting, anomaly detection, classification, NLP, semantic search, and generative AI. You’ll perform feature engineering, experiment design, model tuning, offline/online validation, and integration of models into scalable product architectures. You’ll enhance LLM-based workflows (prompt design, retrieval-augmented generation, vector search, guardrails, response quality evaluation) and strengthen the engineering backbone around AI via CI/CD, monitoring, observability, testing, and model lifecycle automation to ensure reliability, cost-awareness, security, and enterprise-scale readiness.