2 hours ago
Shanghai, ChinaStaff+
Responsibilities
- Design end-to-end AI and GenAI solution architectures, including predictive ML, recommendation engines, RAG applications, AI copilots, intelligent automation, and multimodal AI.
- Define reusable patterns for LLM integration, prompt orchestration, vector search, embedding pipelines, agentic workflows, and model serving.
- Translate business requirements into solution blueprints, architecture diagrams, integration patterns, and implementation guidance.
- Establish AI reference architectures, design principles, architecture decision records, technical review gates, and enterprise governance practices.
- Define architecture across data ingestion, feature preparation, embedding generation, retrieval, inference, evaluation, monitoring, and feedback loops.
- Guide MLOps and LLMOps practices, including model lifecycle management, prompt and version control, automated evaluation, CI/CD, observability, and performance monitoring.
- Define non-functional requirements covering latency, availability, reliability, throughput, cost efficiency, and operational supportability.
- Design controls for privacy, cybersecurity, data residency, PII protection, role-based access, masking, audit logging, human review, content safety, and prompt-injection protection.
- Partner with business teams, data and platform architects, legal, security, compliance, governance stakeholders, engineers, product teams, and vendors.
- Provide technical leadership and mentorship while communicating AI trade-offs, risks, and recommendations to technical and non-technical stakeholders.
Requirements
- 10+ years of professional experience in technology, software engineering, data engineering, AI/ML, or solution architecture.
- 5+ years of experience in solution architecture, enterprise architecture, or technical leadership roles.
- 3+ years of hands-on or architecture experience with AI/ML solutions in production environments.
- Practical experience with GenAI/LLM solutions such as RAG, AI chatbots or copilots, knowledge assistants, semantic search, or AI workflow automation.
- Experience working in complex enterprise environments with cross-functional teams, global stakeholders, and external technology vendors.
- Strong understanding of AI/ML fundamentals, model lifecycle, data science workflows, model serving, evaluation, and monitoring.
- Hands-on experience with GenAI architecture patterns, LLM APIs, embeddings, vector databases, retrieval orchestration, prompt engineering, and agent-based workflows.
- Experience with Python, APIs, microservices, containerization, cloud-native solution design, lakehouse architecture, data pipelines, real-time or batch integration, and governed data products.
- Familiarity with MLOps and LLMOps tools and practices, including MLflow, model registries, CI/CD pipelines, automated evaluation, monitoring, and observability.
- Experience with cloud platforms such as Alibaba Cloud and AWS; Alibaba AI stack experience is a plus.
- Experience with vector databases or semantic-search technologies such as Azure AI Search, FAISS, Milvus, Pinecone, Elasticsearch, OpenSearch, or equivalent technologies.
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About Adidas
Inspired by our heritage, our brand is rooted in sports and the culture born from it. Headquartered in Herzogenaurach, Germany, we’re a global leader in the sporting goods industry, employing 62,035 worldwide.