Objective of job :

Contribute to the future of Mercedes-Benz Financial Services China by building a secure, scalable and reliable AI platform that accelerates enterprise AI adoption. As part of the AI Hub team, the position is responsible for designing, operating and continuously improving the platform capabilities, tooling and standards that enable AI, Gen AI and data-driven solutions to move from experimentation to production efficiently, safely and cost-effectively.

•           Design, build and maintain the enterprise AI platform foundation, including on-prem enterprise model / LLM gateway, orchestration, workflow automation, observability and integration capabilities.

•           Technical owner of the Financial Services China Microsoft Power / SharePoint platform, responsible for their day-to-day operation, maintenance and change request development.

•           Establish and continuously improve LLMOps practices, monitoring and release processes for AI applications.

•           Provide reusable platform services and engineering standards to help AI Software Engineers, data engineers and business teams deliver AI use cases faster with lower operational risk.

•           Ensure the AI platform is secure, compliant, observable, scalable and maintainable in on-premises or regulated enterprise environments.

•           Optimize performance and cost, including resource planning, utilization, capacity management and platform automation.

•           Contribute to AI architecture, security, governance and technology forums as a platform subject matter expert.

This role combines AI platform engineering responsibilities with ownership of selected enterprise productivity platforms, especially Microsoft Power Platform and SharePoint, where these platforms support automation, workflow digitization and AI-enabled business processes.

Qualification

Education:

Bachelor’s degree or equivalent professional experience.

Experience

5+ years hands-on experience in software engineering, DevOps/SRE, data platform engineering or AI/ML engineering. - Practical experience building, operating or supporting production-grade AI/ML, data or application platforms in enterprise environments. O Specific Knowledge - Strong knowledge of DevOps/SRE practices, automation and production operations. - Hands-on experience with containers and orchestration technologies such as Docker and Kubernetes, and familiarity with Linux-based enterprise infrastructure. - Familiarity with LLM Ops concepts, including model deployment, monitoring, observability, evaluation, and rollback mechanisms. - Good understanding of LLM application stack and AI engineering patterns, such as RAG, embeddings, vector databases, workflow orchestration and API-based model serving. - Proficiency in at least one programming or scripting language commonly used for automation and platform engineering, such as Python, Java or Go. - Strong communication and collaboration skills, with the ability to translate platform capabilities and constraints to AI engineers, architects, security teams and business stakeholders. - Financial services, banking, or China-regulated enterprise IT experience is a plus.