Corporate Functions AI Strategy and Adoption Lead, Director

About the role

DWS is looking for a Director-level AI Strategy and Adoption Lead for its Corporate Functions team in Madrid. The role sits at the intersection of hands-on delivery and strategic influence: working with engineers, analysts and delivery leads to identify AI use cases, build practical examples and coach teams, while turning those learnings into recommendations for leadership on tooling, governance, roadmaps and ways of working. You report to the Head of Corporate Functions Technology and work closely with the DWS AI Centre of Excellence.

Key responsibilities

• Act as the primary AI champion in delivery and operational teams, embedding AI into everyday practice.
• Lead the AI Open House and adoption programmes, curating peer wins, running skill-building sessions and removing blockers.
• Coach teams on prompt engineering, gen-AI features, RAG pipelines and agentic workflows.
• Run lightweight experiments such as challenges, hackathons and discovery sprints.
• Convene the Corporate Functions AI Community of Practice and maintain a shared knowledge base of use cases, prompts and lessons learned.
• Advise business stakeholders on how AI applies to their domains and translate technical capability into business value.
• Build, test and iterate on AI tools and prototypes, and design end-to-end solutions such as RAG pipelines, LLM-powered apps and agentic workflows.
• Evaluate internal and SaaS AI models and tooling, including LLMs and agentic frameworks, and recommend grounded choices.

Required profile

• Hands-on software development experience and credibility with engineering teams.
• Track record of driving AI adoption at team level and measuring results.
• Experience with technology teams in complex organisations, ideally in financial services or similar regulated environments.
• Ability to work across levels, from coaching junior developers to advising senior leadership.
• Strong change management skills and awareness of how regulated financial institutions approach AI.

Required skills

• LLMs, prompt engineering and AI-assisted coding tools such as GitHub Copilot.
• At least one delivered RAG implementation, agentic workflow, fine-tuned model or production LLM integration.
• Python (strongly preferred).
• LLM APIs such as OpenAI, Anthropic or Azure OpenAI, and building applications on top of them.
• Vector databases, embedding models, orchestration frameworks and evaluation/observability approaches.
• Agentic AI patterns, including tool use, planning and multi-agent coordination.