About the Role
SAP is seeking a Senior Machine Learning Engineer to join its team in Riyadh, Saudi Arabia. The company focuses on building innovative solutions that impact numerous industries and global commerce. In this role, you will be responsible for leading the design and delivery of production-grade Machine Learning and Generative AI systems to address complex product and business challenges at scale. You will oversee the architecture and implementation across the entire ML lifecycle, including data pipelines, feature stores, training workflows, model-serving infrastructure, online experimentation, and observability.
This position offers an opportunity to contribute to the advancement of AI at SAP, directly influencing performance, reliability, latency, and cost efficiency in live environments. You will drive advanced use cases in deep learning, NLP, ranking, recommendation, forecasting, semantic retrieval, and LLM applications, such as RAG, tool use, evaluation harnesses, and safety controls. Additionally, you will guide technical direction, mentor engineers, and establish best practices as reusable platform capabilities.
Key Responsibilities
- Lead the design and delivery of production-grade ML and generative AI systems to solve complex product and business problems at scale.
- Own the 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.
- Directly shape performance, reliability, latency, and cost efficiency in live environments.
- 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.
- Mentor other engineers and translate best practices into reusable patterns and platform capabilities.
- Define robust evaluation and governance strategies, including offline benchmarking, online experimentation, hallucination analysis, model risk assessment, and responsible AI practices.
Qualifications and Requirements
- Expert-level programming skills in Python.
- Strong software engineering expertise in languages such as Java or Go, enabling the construction of scalable, production-grade systems.
- Deep knowledge of machine learning, deep learning, and optimization techniques across structured data, NLP, search, ranking, and recommendation problems.
- Extensive experience designing and operating end-to-end ML systems, from data ingestion and experimentation to deployment, observability, and lifecycle management.
- Strong hands-on experience with modern ML and LLM tooling, including PyTorch, TensorFlow, scikit-learn, 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, including model registries, feature stores, CI/CD, infrastructure as code, experiment tracking, and automated validation.
- 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.
- Ability to combine strong product judgment with technical depth, translating ambiguous business problems into scalable, high-impact AI solutions.
- Capacity to thrive in complex, fast-moving environments and bring clarity, ownership, and strategic thinking to drive long-term platform success.
- Demonstrated curiosity, ownership, and a commitment to continuous learning.
- Customer-centricity and motivation by meaningful outcomes.
- Comfort navigating ambiguity in a fast-evolving AI landscape.
Technical Skills
- Programming Languages: Python, Java, Go
- Machine Learning & Deep Learning: Machine Learning, Deep Learning, Optimization Techniques, NLP, Search, Ranking, Recommendation
- ML Systems: ML Systems Design, ML Systems Operation, Data Ingestion, Experimentation, Deployment, Observability, Lifecycle Management
- ML/LLM Tooling: PyTorch, TensorFlow, scikit-learn, Fine-tuning, Model Evaluation, Orchestration, Model Serving
- Generative AI: Generative AI Applications, Embeddings, Vector Databases, RAG Pipelines, Agent Workflows, Prompt Engineering, Guardrails
- MLOps & Platform Engineering: MLOps, Platform Engineering, Model Registries, Feature Stores, CI/CD, Infrastructure as Code, Experiment Tracking, Automated Validation
- Distributed Systems & Cloud: Distributed Systems, Event-Driven Services, Streaming Data, Cloud-Native ML Platforms
- Optimization: Performance Optimization, Scalability Optimization, Reliability Optimization, Cost Optimization
- Evaluation & Governance: Evaluation Strategies, Governance Strategies, Offline Benchmarking, Online Experimentation, Hallucination Analysis, Model Risk Assessment, Responsible AI
- Soft Skills: Product Judgment, Technical Depth, Problem Solving, Strategic Thinking, Leadership, Teamwork, Curiosity, Ownership, Continuous Learning, Customer-Centricity, Ambiguity Navigation
Work Environment and Details
This is a full-time, regular position based in Riyadh, Saudi Arabia. The role requires 5-10 years of experience and involves 0-10% travel. SAP is committed to fostering an inclusive culture that prioritizes health and well-being, offering flexible working models. The company believes that unique capabilities and qualities strengthen its organization and invests in employees to help them realize their full potential. SAP is committed to Equal Employment Opportunity and provides accessibility accommodations to applicants with disabilities.
