Senior Machine Learning Engineer

at  SAP

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 (ERP) software, SAP has evolved to become a market leader in end-to-end business application software and related services for database, analytics, intelligent technologies, and experience management. As a cloud company with two hundred million users and more than one hundred thousand employees worldwide, we are purpose-driven and future-focused, with a highly collaborative team ethic and commitment to personal development. Whether connecting global industries, people, or platforms, we help ensure every challenge gets the solution it deserves. At SAP, you can bring out your best.

WHAT YOU’LL BRING

We are looking for a candidate who possesses a passion for innovation and data-driven solutions paired with a hands-on can-do attitude. The ideal candidate will be a self-motivated expert with a love for delivering productive enterprise-grade ML services and who helps coaching junior ML engineers

Ideal candidates have the following experience/skills :

• University degree in a technical field
• Strong practical Machine Learning and computer science skills.
• Applied experience in python, Kubernetes.
• Hands-on experience in PostgreSQL, Spark, Elasticsearch
• Familiar with modern DevOps methodologies
• Fluent in English, both verbal and written communication skills
• Creative thinking, willingness, and ability to learn quickly new concepts and technologies.

As a Senior Machine Learning Engineer in Singapore, you will be part of a team that delivers end-to-end AI solutions integrated in the processes of our customer support business to deliver support in faster and smarter way. In your daily job you will:

• Assess requirements necessary for developing ML services in the Support Delivery domain.
• Take ML models/algorithms from conception to production, by designing and implementing ML microservices and pipelines in production environment.
• Maintain and continuously optimize live productive ML services with a focus on scalability, availability, and reliability.
• Setup and maintain CI/CD pipelines for ML services.
• Design and develop large scale training and deployment systems.
• Strive for automation and cloud operation readiness.
• Improve quality and system scalability through continuous evaluation, analysis, and refinement of the system implementation.
• Collaborate with data scientists and data engineers to deliver ML models in productive services.