Manager, AI Engineering

Company description Publicis Groupe is a global leader in communication and is positioned at every step of the value chain, from consulting to execution, combining marketing transformation and digital business transformation. Through a unified and fluid organization, Publicis Groupe partners with its clients across communication, media, data and technology to ensure access to the full breadth of capabilities in every market. Present in over 100 countries, Publicis Groupe employs around 103,000 professionals. Overview The Manager, AI Engineering is a hands-on technical leader responsible for building and maintaining the data and technology capabilities that power MRCL's skill ecosystem. This role owns the requirements process for data and tech integrations, and is one of the primary builders and stewards of skills within the MRCL platform — ensuring they are well-constructed, reliable, and aligned to the needs of marketing teams across Publicis Groupe. The Manager sits at the boundary between technical execution and business need, working closely with AI strategy, data, and product stakeholders to turn requirements into functioning platform capabilities. Responsibilities Own the end-to-end process for gathering, documenting, and prioritizing data and technology requirements for MRCL platform features and integrations Design, build, and maintain Kitchen skills within the MRCL platform, ensuring they are well-structured, tested, and deliver high-quality, repeatable outputs for end users Partner with AI and data strategy teams to understand capability needs and translate them into technical specifications and buildable solutions Maintain a clear view of the skills ecosystem — identifying gaps, redundancies, and opportunities for improvement across the skill library Work closely with the SVP of Engineering on infrastructure dependencies that affect skill performance and data connectivity Support onboarding and enablement of internal teams on how to effectively use and contribute to the MRCL skill ecosystem Lead the strategic adoption and operationalization of AI technologies across the MRCL platform, including LLM-powered applications, agentic workflows, and AI services, ensuring scalable, secure, and enterprise-grade deployment. Establish governance and best practices for AI systems, including model lifecycle management, observability, evaluation frameworks, responsible AI standards, and integration with enterprise data and security controls Qualifications Hands-on experience in software, data or platform engineering with an ability to build and maintain technical integrations Proficiency in requirements gathering, technical documentation, and translating business needs into engineering specifications Practical knowledge of skill development and prompt engineering within AI platforms Solid understanding of data architecture, APIs, and system connectivity Detail-oriented with strong quality assurance instincts — experienced in testing protocols and version control practices Effective collaborator across technical and strategic stakeholders, able to bridge engineering and business perspectives Self-starter who can manage multiple workstreams and prioritize effectively in a fast-moving platform environment Additional information Our Publicis Groupe motto “Viva La Différence” means we’re better together, and we believe that our differences make us stronger. It means we honor and celebrate all identities, across all facets of intersectionality, and it underpins all that we do as an organization. We are focused on fostering belonging and creating equitable & inclusive experiences for all talent. Publicis Groupe provides robust and inclusive benefit programs and policies to support the evolving and diverse needs of our talent and enable every person to grow and thrive. Our benefits package includes medical coverage, dental, vision, disability, 401K, as well as parental and family care leave, family forming assistance, tuition reimbursement, and flexible time off. If you require accommodation or assistance with the application or onboarding process specifically, please contact [email protected]. All your information will be kept confidential according to EEO guidelines. Veterans Encouraged to Apply. #LI-AM3

Hands-on experience in software, data or platform engineering with an ability to build and maintain technical integrations Proficiency in requirements gathering, technical documentation, and translating business needs into engineering specifications Practical knowledge of skill development and prompt engineering within AI platforms Solid understanding of data architecture, APIs, and system connectivity Detail-oriented with strong quality assurance instincts — experienced in testing protocols and version control practices Effective collaborator across technical and strategic stakeholders, able to bridge engineering and business perspectives Self-starter who can manage multiple workstreams and prioritize effectively in a fast-moving platform environment

Own the end-to-end process for gathering, documenting, and prioritizing data and technology requirements for MRCL platform features and integrations Design, build, and maintain Kitchen skills within the MRCL platform, ensuring they are well-structured, tested, and deliver high-quality, repeatable outputs for end users Partner with AI and data strategy teams to understand capability needs and translate them into technical specifications and buildable solutions Maintain a clear view of the skills ecosystem — identifying gaps, redundancies, and opportunities for improvement across the skill library Work closely with the SVP of Engineering on infrastructure dependencies that affect skill performance and data connectivity Support onboarding and enablement of internal teams on how to effectively use and contribute to the MRCL skill ecosystem Lead the strategic adoption and operationalization of AI technologies across the MRCL platform, including LLM-powered applications, agentic workflows, and AI services, ensuring scalable, secure, and enterprise-grade deployment. Establish governance and best practices for AI systems, including model lifecycle management, observability, evaluation frameworks, responsible AI standards, and integration with enterprise data and security controls