Publicis Groupe is seeking an SVP, Technical Solution Architecture to lead the hands-on design, prototyping, and implementation of advanced data, AI, and platform solutions across high-priority clients.
This role sits at the intersection of architecture, build, and client partnership. The ideal candidate is equally comfortable whiteboarding with engineers, prototyping workflows themselves, and translating complex systems into clear, compelling value stories for senior client stakeholders.
Key Responsibilities
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Own end-to-end solution architecture, from concept through working implementation, not just design artifacts
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Personally prototype and build core components of data, AI, or platform solutions (POCs, workflows, integrations, automation layers)
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Translate client business problems into concrete system designs, technical workflows, and architectural decisions
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Lead hands-on development of AI-enabled workflows, data pipelines, or platform capabilities in partnership with engineering teams
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Make and stand behind build decisions — tools, platforms, tradeoffs, and implementation approach
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Serve as the technical face of MRCL-enabled solutions in client conversations, including deep, detailed discussions
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Partner with product, data science, and engineering leaders to ensure solutions are viable, scalable, and deliverable
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Produce clear technical documentation, diagrams, and build artifacts that teams can execute against
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Act as escalation point for solution feasibility, complexity, and delivery risk
What “Hands-On” Means in This Role
The successful candidate:
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Can personally wire together systems, logic, or workflows (even if not shipping production code alone)
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Understands how platforms actually behave in practice — limits, latency, governance, cost
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Is comfortable building prototypes or reference implementations that others can extend
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Does not operate purely as a liaison or reviewer
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12+ years designing and building complex data, AI, marketing technology, or platform solutions
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Demonstrated experience architecting and implementing systems in cloud environments
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Hands-on experience with components across:
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Data platforms or pipelines
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AI / ML or GenAI-enabled workflows
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Martech or enterprise data ecosystems
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Ability to switch between technical depth and executive-level communication seamlessly
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Strong client presence with the credibility to lead technical conversations directly
Success Looks Like
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Clients trust you because you understand how things actually work
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Engineering teams trust your designs because they’re practical and buildable
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Stakeholders rely on you to determine what is possible vs. what is theoretical
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Solutions move faster because fewer layers are needed between intent and execution
