Sr AI​/Machine Learning Engineer - SAP BTP Fabric

SAP BTP Fabric helps our customers and internal developers to create platform services including platform services for Agents. BTP Fabric is growing immensely by provisioning base platform for services within SAP as well for Agents. BTP Fabric is evolving into a Machine Learning powerhouse, utilizing the power of AI to build platform, for current day services; as well as building the next-generation platform for Agentic solutions.

As an AI / Machine Learning Engr. on SAP BTP Fabric team, you can accelerate the innovation, design, and delivery of the next generation of platform experiences with Machine Learning and Generative AI. Applications running on the Network provide critical business processes involving thousands of buying organizations, millions of sellers across the globe, and several millions of users.

Duties and responsibilities of an AI / Machine Learning Engineer within SAP BTP Fabric will include work at the bleeding edge of generative AI, Agentic AI, and Semantic Vector Search including working with open-source as well as third-party generative models. In addition, Machine Learning Engrs. within SAP BTP Fabric will work with conventional, open-source, reasoning as well as basic LLMs for adapted use for POC business use cases through fine-tunning, distillation and/or quantization to enable serving customers at scale.

Responsibilities will also involve out-of-box utilization of SOTA LLMs to enable Platform capabilities that can be adopted by several LOBs including:

• Design and implement innovations in the Semantic Vector Search space
• Develop, test, deploy GPU enabled microservices that provide BIC metrics and support measurable improvement in Business KPIs
• Work in and develop core agentic platform components for BTP
• Master framework design and modeling in a large scale, multi-tenant, on-demand environment
• Proactively detect and solve scalability and performance issues
• Work across teams and functions to build best-in-class solutions for various stakeholders

• Master’s degree in Computer Science, Data Science, Machine Learning, with equivalent work experience.
• 5+ years experience developing production-grade, scalable, multi-threaded python API, Agentic and web applications
• Advanced compute acceleration expertise with GPU acceleration, vector programming.
• Advanced knowledge of Nvidia or AMD GPU architecture and acceleration capabilities and provisioning across cloud enterprises.
• Working knowledge of cloud-native architecture and cloud deployment frameworks, including microservices, containerization, Jenkins deployment pipeline, Docker files, Github actions & K8s.
• Capable of application performance analysis, tuning as well as latency enhancements with vector programming.
• Knowledgeable in application security best practices, networking, and data communication
• Proficient in Python, RUST, Linux, HTML, and REST APIs with either Flask, FAST APIs etc.
• Proficiency in building production-grade Machine Learning, Artificial Intelligent products as well as deep knowledge on using Agents.
• Proficiency with Databricks, github fundamentals.
• An understanding of the basics of Encoder and Decoder models including Generative LLM models.
• Cybersecurity fundamentals including foundational knowledge of Data Protection and Privacy.

• Experience working with and deploying and utilizing LLM models in production.
• Experience with Claude code, Cline, Github copilot as developer accelerators etc.
• Experience with cloud platforms such as GCP, AWS, and Azure.
• Experience with MLOPs, LLMOPs and/or genAIOPs

Compensation Range Transparency
: SAP believes the value of pay transparency contributes towards an honest and supportive culture and is a significant step towards demonstrating SAP’s commitment to pay equity. SAP provides the annualized compensation range inclusive of base salary and variable incentive target for the career level applicable to the posted role. The targeted annual combined range for this position is USD .The

actual amount to be offered to the successful candidate will be within that range, dependent upon the key aspects of each case which may include…