Bei Roche kannst du ganz du selbst sein und wirst für deine einzigartigen Qualitäten geschätzt. Unsere Kultur fördert persönlichen Ausdruck, offenen Dialog und echte Verbindungen. Hier wirst du für das, was du bist, wertgeschätzt, akzeptiert und respektiert. Dies schafft ein Umfeld, in dem du sowohl persönlich als auch beruflich wachsen kannst. Gemeinsam wollen wir Krankheiten vorbeugen, stoppen und heilen und sicherstellen, dass jeder Zugang zur Gesundheitsversorgung hat – heute und in Zukunft. Werde Teil von Roche, wo jede Stimme zählt.
Die Position
As a key driver of technological innovation, you will leverage cutting-edge AI technologies and advanced architectures to deliver strategic business value for Roche China. You will lead the end-to-end design, development, iterative enhancement, and ecosystem integration of the AI infrastructure, platform, and application adoption layers, ensuring all systems strictly adhere to enterprise security and compliance standards.
We are seeking a visionary architect who combines enterprise-level system design, platform engineering methodologies, and agile delivery with profound AI expertise. Your ultimate mission is to translate state-of-the-art AI capabilities into transformative, real-world healthcare solutions. In this role, you will be responsible for:
AI Platform Layer Design, Development, and Management
- Kubernetes Cluster Management: Lead the deployment, administration, and full lifecycle management of Kubernetes clusters, including node scaling, upgrades/rollbacks, certificate rotation, RBAC administration, and cluster teardowns. Develop robust automation code for cluster operations and drive federated cluster management initiatives.
- MLOps Platform Engineering: Architect, build, and manage MLOps platforms using open-source (e.g., Kubeflow, MLFlow) or commercial (Dataiku, Databricks) frameworks. Oversee the ML lifecycle by integrating core components (WebIDE, FeatureStore, ModelRegistry, AutoML, Pipelines, Model Serving) and implementing metrics for CMCT and Drift Detection.
- LLMOps Architecture: Design and develop platforms to support the Large Language Model (LLM) lifecycle. Integrate critical LLMOps components (Data Assets, Model Cards, SFT, OpenAI-Compatible Inference) and operationalize mainstream fine-tuning and inference frameworks (DeepSpeed, TorchTune, vLLM, SGLang).
- AI Gateway& Governance: Design and implement enterprise-level AI gateways and integration platforms. Establish governance frameworks (FinOps, Guardrails, MAAS PrivateLink) and integrate with domestic and international MAAS ecosystems (GCP Vertex AI, AWS Bedrock, Azure OpenAI, Ali DashScope).
- Familiarity with at least one major large-scale model training framework (e.g., Megatron-LM or MindSpore).
- Expertise inRay for both distributed training and inference pipelines.
- Strong understanding of cross-node/multi-node scaling and distributed systems, essential for supporting next-generation Large Language Models (LLMs).
AI Application Layer Design, Development, and Management
- AI& Agentic Application Development: Architect, design, and develop highly scalable AI and Agentic applications, leveraging mainstream frameworks such as LangChain and LangGraph.
- System Integration: Integrate applications across advanced platform tools (Kubernetes, Slurm, MLOps, LLMOps, AI Gateway) and diverse data ecosystems (S3, RDBMS, VectorDB, GraphDB).
- Microservices Architecture& DDD: Drive Domain-Driven Design (DDD) to decompose APIs based on complex business requirements, defining clear service boundaries, contracts, and data models.
- Team Leadership& Delivery: Organize, lead, and mentor engineering teams to successfully deliver multi-format services, including APIs, WebUIs, SDKs, CLIs, and APPs.
- Engineering Excellence: Establish and enforce rigorous coding standards, comprehensive unit/integration testing protocols, and robust code review processes.
- Backend System Design: Design and optimize backend systems utilizing diverse API standards (REST, gRPC, GraphQL), database technologies (SQL, NoSQL), messaging systems (Kafka, RabbitMQ), and caching mechanisms.
- Architectural Strategy: Evaluate and implement appropriate architectural patterns (such as Event-Driven, CQRS, DDD, and Serverless) by conducting thorough trade-off analyses based on project needs.
- End-to-End Execution: Oversee the full lifecycle of AI software development, ensuring successful implementation and maintaining a track record of high-quality product delivery.
Platform Engineering, SRE, and Enterprise Integration
- Cloud-Native& Reusability Engineering: Drive platform hardening and component reusability engineering by leveraging mainstream CNCF projects (Prometheus, Grafana, OpenTelemetry, Ray, ELK, Kubeflow).
- Observability& FinOps: Design and integrate comprehensive observability solutions (Metrics, Logs, Tracing) and alerting systems to ensure transparent monitoring of application performance, usage, and FinOps costs.
- Non-Functional Requirements Assurance: Ensure all architectures strictly adhere to enterprise standards for high availability (HA), disaster recovery/rollback, security compliance, and component reusability.
- Enterprise Integration: Execute seamless integrations with the wider enterprise IT ecosystem, including SSO, LDAP, Git repositories, Artifact repositories, Cloud Landing Zones, Cloudflare, and AI Gateways.
AI Infrastructure Layer Design, Development, and Management
- Hardware& Storage Strategy: Evaluate, benchmark, and select AI infrastructure hardware (GPU and high-performance CPU bare metal servers), storage solutions (NAS, SAN), and coordinate physical data center requirements (power distribution, air/liquid cooling).
- Network Planning& Optimization: Architect network topologies and subnets, managing out-of-band networks, business networks, and high-performance RDMA passthrough networks (InfiniBand, RoCEv2). Deploy centralized and automated monitoring for all network equipment.
- OS& Toolchain Hardening: Administer Linux distributions and design automated security hardening protocols for large-scale server clusters. Manage supercomputing drivers and complete toolchains (Ansible, Packer, NVIDIA full-stack, RDMA full-stack, storage drivers, containerization).
- Cloud Infrastructure Automation: Manage enterprise-level private cloud environments, ensuring robust IAM, data center direct-connect, cloud service security, and data encryption. Drive Infrastructure as Code (IaC) automation utilizing Terraform, CloudFormation, and CLI tools.
- Privileged Access Management: Configure, manage, and maintain enterprise-level bastion hosts and oversee super-user access controls.
Demand Management, Process Design, and Agile Development
- Project& Delivery Management: Drive large-scale, role-based project execution applying standard Project Management Methodologies (PMM), ensuring alignment with pharmaceutical industry compliance standards.
- Agile Leadership: Champion Agile and Scrum methodologies. Perform WBS decomposition, manage project backlogs in Jira, and coordinate cross-team delivery through CI/CD pipelines (GitLab/GitHub, JFrog Artifactory).
- Governance& Process Design: Collaborate on the design and implementation of product-related operational processes (user training/certification, workspace/model/data asset lifecycle management, system disclaimers) to guarantee system safety and compliance.
Collaborations
- Project Documentation: Author and finalize critical architectural documents, including Solution Architectures, LLM specifications, and Installation Validation protocols.
- Knowledge Base Maintenance: Create, update, and govern comprehensive technical documentation across platforms like GitLab MD, Confluence, and GitLab Pages, including drafting detailed process design documents.
- Cross-Functional Team Leadership: Facilitate highly efficient collaboration across global and local cross-functional teams, ensuring seamless communication in both fluent Chinese and English.
- Mentorship: Providing architectural guidance and fostering team capability growth.
- Drive and lead efficient collaboration across multi-functions, global/local teams with fluent Chinese and English communication.
Qualifications
- 10+ years of experience as Solution Architect, with a minimum of 2+ years of hands-on experience specifically in AI Architecture or AI Platform design.
- Fluent bilingual capabilities in both English and Mandarin , enabling seamless, high-level architectural discussions with both global and local technical teams.
- Experience in the pharmaceutical, life sciences, or top-tier tech industries (with large-scale compute platform exposure) is highly preferred.
- Familiar with GxP relevant architecture design experience is highly preferred
- Deep mastery of the Kubernetes ecosystem and full-lifecycle cluster management.
- Extensive experience designing and building MLOps and LLMOps platforms (familiarity with Kubeflow, MLflow, Databricks, etc.).
- Deep knowledge of core ML components including Feature Stores, Model Asset Management, SFT (Supervised Fine-Tuning), and Model Serving.
- Familiarity with mainstream large model fine-tuning and inference acceleration frameworks (e.g., DeepSpeed, vLLM, SGLang, TorchTune etc.).
- High proficiency in RAG architecture design.
- Hands-on experience developing Agentic applications using mainstream frameworks such as LangChain and LangGraph.
- Familiarity with Vector Databases (VectorDB) and Graph Databases (GraphDB).
- Rich experience in High Availability (HA) system design, Disaster Recovery, RBAC, and integrating enterprise-level components (e.g., SSO, LDAP, Bastion hosts).
- Proven ability to ensure that architectural designs strictly adhere to security compliance standards and FinOps governance requirements.
- In-depth understanding of Cloud-Native architectures (e.g., AWS, Alibaba Cloud).
- High proficiency in the CNCF mainstream open-source technology stack, with particular expertise in observability tools (e.g., Prometheus, Grafana, OpenTelemetry, ELK).
- A strong platform engineering mindset with extensive hands-on experience using Terraform, Ansible, Packer, and CloudFormation for automated deployment and system security hardening.
- Mastery of at least one core backend development language (e.g.Python, Go).
- Expertise in building robust, end-to-end pipelines utilizing tools such as GitLab CI/CD and JFrog.
- Strong familiarity with foundational AI hardware (GPU/high-performance CPU) and storage technologies (NAS/SAN).
- Proven ability to conduct performance benchmarking and hardware selection for high-compute clusters.
- Profound understanding of data center network architectures, with expertise in planning and configuring out-of-band networks and high-performance RDMA passthrough environments.
- Mastery of Linux (RHEL/CentOS) operating systems and high proficiency in configuring the NVIDIA driver full-stack and supercomputing driver toolchains.
- High proficiency in Scrum/Agile development processes and necessary tools.
- Demonstrated track record of leading large-scale AI platforms or projects and mentoring cross-functional teams to successful delivery.
Wer wir sind
Eine gesündere Zukunft treibt uns zur Innovation an. Mehr als 100.000 Mitarbeiter weltweit arbeiten gemeinsam daran, wissenschaftliche Fortschritte zu erzielen und sicherzustellen, dass jeder Zugang zur Gesundheitsversorgung hat – heute und für zukünftige Generationen. Durch unser Engagement werden über 26 Millionen Menschen mit unseren Medikamenten behandelt und mehr als 30 Milliarden Tests mit unseren Diagnostik-Produkten durchgeführt. Wir ermutigen uns gegenseitig, neue Möglichkeiten zu erkunden, Kreativität zu fördern und hohe Ziele zu setzen, um lebensverändernde Gesundheitslösungen zu liefern.
Gemeinsam können wir eine gesündere Zukunft gestalten.
Roche ist ein Arbeitgeber, der die Chancengleichheit fördert.
