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Machine Learning Engineering ManagerSAP · Pasir Panjang, Central Region
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Machine Learning Engineering Manager

🗓️ Posted 2026-10-06 Pasir Panjang, Central Region Full-time Hybrid ICT
💰 Salary Not shared 📊 Market S$18,000–S$28,000/mo

About the Company

SAP Labs Singapore is the 20th engineering hub in the SAP Labs Network - SAP's newest innovation hub driving product leadership, talent development, and ecosystem engagement. SAP Labs Singapore has the ambition to become SAP's AI Hub, leveraging deep competencies across its engineering teams to shape the next generation of enterprise AI experiences.

About the Role

As the Machine Learning Engineering Manager for the Joule Work Platform Services team, you will lead efforts to build a scalable, enterprise grade platform powering AI driven work experiences across SAP's product portfolio. Your responsibilities span designing and developing platform services that enable Joule's core capabilities - including conversational AI, agentic workflows, tool integrations, and seamless connectivity to SAP business applications such as S/4HANA, SuccessFactors, and Supply Chain Management. You will define a compelling platform vision and lead your teams to execute on it - from foundational service architecture through to reliable, production grade delivery. As a platform engineering leader, you will represent your team's technical direction to internal and external stakeholders, including product leadership, partner teams, and customers. You will collaborate closely with development leads, AI/ML engineers, product owners, product managers, and DevOps colleagues - both within your team and across adjacent units - to deliver platform capabilities that accelerate Joule adoption: from agent development and evaluation frameworks through to scalable deployment and continuous improvement in production.

Responsibilities

  • ● Define product plans and execute against committed delivery timelines, managing the day to day activities of the development team.
  • ● Own the complete software development lifecycle, designing, leading implementation, and testing new platform features to ensure adoption by stakeholders in a fast paced, agile environment.
  • ● Lead the design and delivery of agentic AI services, evaluation frameworks, and LLM integrated platform capabilities that form the foundation of the Joule Work experience.
  • ● Manage customer expectations through product support, ensuring high standards in incident processing, quality of interactions, and timely resolution within SLA commitments.
  • ● Provide architectural and functional guidance to the team, maintaining hands on engagement to deliver highly available, scalable AI/ML platform services.
  • ● Lead and develop your team through active engagement, staffing, talent management, career development, mentoring, and skills growth, building and sustaining a best in class AI engineering team.
  • ● Contribute to and drive location level initiatives including cross unit collaboration, training plan execution, hiring and internship management, and budget planning.

Requirements

  • ● A degree in Computer Science, Data Science, Mathematics, Statistics, or a related field.
  • ● Eight or more years of experience in software product development, with a strong focus on AI/ML systems and platform engineering.
  • ● A proven track record of building a culture of design excellence and delivering high impact AI powered products.
  • ● Strong command of Agile development processes, with hands on experience implementing agile practices across Development, QA, and Release functions.
  • ● Ability to communicate vision and strategy clearly to engineering teams, partners, and customers.
  • ● Demonstrated ability to build cohesive teams and drive efficient cross functional collaboration.
  • ● Technical depth sufficient to earn the respect of a senior engineering team and make credible architectural decisions.
  • ● Strong organisational skills with comfort leading change in a fast moving environment.
  • ● SaaS delivery based on microservice architecture.
  • ● Container technologies: Docker and Kubernetes.
  • ● Cloud deployment on AWS, GCP, or Azure.