Responsibilities

• Conduct comprehensive assessments of current marketing workflows, including cross-team handoffs, operational dependencies, and data flows.
• Map end-to-end marketing and communications processes, identifying bottlenecks, redundancies, and automation opportunities.
• Understand the data landscape, including sources, quality, and integration points, to inform AI embedding strategies.
• Work closely with the engineering-side AI architecture team to understand the to-be AI architecture and how manual or semi-automated marketing processes are being transformed into intelligent, automated solutions — ensuring marketing's business requirements and enterprise-alignment needs are reflected in that design.
• Maintain a strong working knowledge of the AI solutions being built (LLMs, retrieval-augmented generation, recommendation engines, and other generative AI tools) so enterprise standards, governance, and platform requirements can be mapped onto them — without duplicating the build-side design and development work.
• Drive the onboarding and integration of AI agents, tools and applications onto the MarTech stack to enable business processes — spanning both in-house solutions and third-party/bought AI, where this role leads vendor evaluation, build-vs-buy, and enterprise-compliant integration.
• Review and pressure-test architecture artifacts (context diagrams, data flow diagrams, interface specifications, solution blueprints) produced by the data science and engineering teams — validating enterprise fit, integration, and governance rather than authoring them.
• Collaborate with Data Engineering teams to design data pipelines that facilitate efficient, low-overhead data flow into and out of AI applications, avoiding unnecessary complexity.
• Ensure data governance, security, and privacy are embedded into all AI solutions.
• Identify and specify API and integration requirements for AI agents, ensuring interoperability within the broader MarTech ecosystem and with third-party AI vendors.

Requirements

• A bachelor's degree in computer science, software engineering, or a related field is required.
• A master's degree or higher in a relevant discipline is preferred.
• Enterprise Architecture & Solution Design: Ability to design scalable, secure, and enterprise-aligned AI and MarTech solutions.
• AI & Data Technologies: Knowledge of modern AI solutions, including large language models (LLMs), generative AI, retrieval-augmented generation, recommendation engines, and AI workflow orchestration.
• Cloud & Infrastructure: Familiarity with cloud platforms (Azure, AWS, GCP), cloud services, networking, environment management, and AI-specific infrastructure components like model endpoints, vector stores, and observability tools.
• Integration & Data Management: Experience with system dependencies, API design, data access patterns, integration standards, and data governance.
• Security & Governance: Understanding of enterprise security practices, privacy, compliance, risk assessment, and vendor due diligence related to AI solutions.
• Communication & Stakeholder Management: Strong ability to communicate complex technical concepts to non-technical stakeholders and influence cross-functional teams.
• Project & Requirements Management: Proven capability to translate business needs into technical requirements, architecture blueprints, and implementation plans.
• Vendor & Platform Evaluation: Experience assessing AI vendors, platforms, and performing technical due diligence.

Preferred

• Certifications (Plus): Certifications in cloud platforms (Azure, AWS, GCP), enterprise architecture frameworks, or AI-related credentials are advantageous.

Benefits

• Health & Wellbeing
• Personal & Professional Development
• Unconditional Inclusion