Senior AI Architect

Purpose & Overall Relevance for the Organization

This role as Senior Manager - AI Architect leads architecture design, technical governance, and solution blueprinting for enterprise AI and Generative AI solutions. The role acts as the horizontal AI architecture authority across multiple projects and delivery pods, translating business opportunities into scalable, secure, compliant, and production-ready AI solutions.

Key Responsibilities

• AI / GenAI Solution Architecture: Design end-to-end AI and GenAI solution architectures, including predictive machine learning, recommendation engines, RAG-based applications, AI copilots, intelligent automation, and multimodal AI scenarios.
• Define reusable architecture 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.
• Evaluate and recommend model strategies, including commercial LLMs, open-source models, local deployment options, and hybrid approaches.
• Enterprise Architecture & Governance: Act as the design authority for AI solutions and establish AI reference architectures, design principles, architecture decision records, and technical review gates.
• Partner with data and platform architects to integrate AI solutions with enterprise lakehouse platforms, data pipelines, APIs, and business applications.
• Drive technical governance for AI solution delivery, including design reviews, production readiness reviews, and vendor technical assessments.
• Data Platform, MLOps and LLMOps Integration: Define architecture for AI workloads 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 model performance monitoring.
• Ensure AI solutions use trusted, governed, and high-quality data sources with appropriate lineage, access control, and monitoring.
• Define non-functional requirements for AI systems, including latency, availability, reliability, throughput, cost efficiency, and operational supportability.
• Security, Compliance and Responsible AI: Design AI solutions in compliance with applicable data privacy, cybersecurity, data residency, and internal governance requirements.
• Define controls for PII protection, role-based access control, data masking, audit logging, human-in-the-loop review, and sensitive data handling.
• Establish GenAI guardrails, including grounding strategy, hallucination mitigation, prompt injection protection, content safety controls, and usage monitoring.
• Work with legal, security, compliance, and data governance stakeholders to identify and mitigate AI risks before production release.
• Stakeholder Engagement & Technical Leadership: Partner with business teams to identify high-value AI use cases and shape feasible, measurable, and scalable solutions.
• Provide technical leadership to AI engineers, data engineers, QA, product teams, and external vendors during solution delivery.
• Communicate complex AI concepts, trade-offs, risks, and recommendations to technical and non-technical stakeholders.
• Mentor project teams and help improve enterprise AI architecture and engineering maturity.

Key Relationships

• Global and local IT
• Respective business function

Minimum Qualifications

• 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.

Technical Skills

• Strong understanding of AI/ML fundamentals, model lifecycle, data science workflows, model serving, evaluation, and monitoring.
• Understanding and hands-on experience with GenAI architecture patterns, including LLM APIs, embeddings, vector databases, retrieval orchestration, prompt engineering, and agent-based workflows.
• Experience with Python, REST APIs, microservices, containerization, and cloud-native solution design.
• Experience with data platforms such as lakehouse architecture, data pipelines, real-time and batch integration, and governed data products.
• Familiarity with MLOps/LLMOps tools and practices such as 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.

Culture and Inclusion

At adidas, employees are encouraged to demonstrate courage, ownership, innovation, teamplay, integrity, and respect. The company emphasizes diversity, equity, and inclusion as core elements of its culture and talent processes.