Lead AI Consultant

Key Responsibilities 

1. Client-Facing Consulting & Delivery 

• Act as primary consultant on AI engagements, advising senior client stakeholders on AI transformation strategy, AI architecture, and implementation. 
• Develop AI strategies, solution concepts, and technical specifications calibrated to each client’s organisational reality, not templated frameworks applied regardless of context. 
• Translate business requirements into technical specifications, and technical constraints into language stakeholders can act on. Both directions matter equally. 
• Facilitate client workshops from AI discovery through implementation planning; contribute to pitches and business development as a subject matter expert. 

2. Strategic Foresight & Scenario Thinking 

• Anticipate how AI will reshape organisational structures, information flows, and decision-making over the next two to five years — advising clients with concrete, time-bound roadmaps. 
• Build forward-looking scenarios that connect emerging capabilities (agentic systems, real-time retrieval, autonomous workflows) to concrete operational change. 
• Challenge inherited assumptions about hierarchies and operating models. Propose AI-native alternatives grounded in feasibility and organisational readiness. 

3. Internal Enablement & Workforce Transformation 

• Support & Lead in-house training and enablement programmes from week one, designing materials calibrated from foundational concepts to advanced agentic workflow design. 
• Serve as a visible example of AI-first practice. Cultural change does not come from slide decks; it comes from colleagues who work differently and produce better results. 

4. AI Practice & Solution Architecture 

• Contribute to the design of AI solutions — agent orchestration, retrieval-augmented generation, multi-agent coordination — strategically and structurally, not only at implementation level. 
• Evaluate and operationalise new AI tools, frameworks, and platforms as they emerge. The expectation is a live understanding of the state of the art, not last quarter’s releases. 
• Support Merkle’s service lines with AI-native expertise, integrating new paradigms into delivery rather than siloing them in a separate innovation function. 

5. Ecosystem Building 

• Sustain partnerships with startups, university research groups, and open-source communities. The frontier of applied AI does not live inside large organisations alone. 
• Help organise technical hackathons and experimentation sprints designed to test ideas before they reach client engagements. 

Requirements:

• Degree in Business, Information Systems, Computer Science, Data, or a closely related technical discipline. An engineering foundation is non-negotiable. 
• Several years of experience in AI consulting, software engineering, or data with direct client interaction and delivery accountability. 
• Professional fluency in English is mandatory and German a nice to have. 
• Strategic foresight over reactive execution. You see where AI is heading and think in future scenarios. An consultant who lives in the present — however skilled — is not the person we are looking for. 

• AI-first orientation. Every workflow begins with the question: can AI do this, and if so, how? You default to yes unless evidence compels otherwise. 

• Deep practical literacy. Generative AI tools, coding assistants, agentic frameworks — used daily, not experimentally. You maintain current proficiency as the ecosystem evolves week by week. 

• Empathy and communicative precision. Technical capability without the ability to listen, understand client constraints, and build trust is insufficient. 

• Self-directed prioritisation. You manage your workload without waiting to be told what matters. 

• A drive to learn that does not depend on external motivation. If the pace of change feels like disruption rather than opportunity, this role will not suit you. 

Nice to have:

• Prior consulting, client-facing technical advisory, or forward-deployed engineering experience. 
• Hands-on experience with agent orchestration frameworks, LLM integration, or AI-native product development. 
• Track record of facilitating workshops or guiding clients through structured discovery. 
• Familiarity with global professional services organisations. 

About this challenge: 

This is not an incremental hire into an established team. We are building something new, and early hires will shape what it becomes. 

• Strategic influence within one of the largest digital transformation networks globally. 
• Direct access to enterprise clients across industries, with autonomy to shape how AI adoption unfolds inside those organisations. 
• An environment engineered for rapid growth: dedicated experimentation time, state-of-the-art tooling, collaboration with leading practitioners. 
• Competitive compensation, flexible working arrangements, and the operational backing of Dentsu’s global infrastructure.