AI Enterprise Senior Technical Lead

VML Enterprise Solutions; Faro, Porto; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

WPP Enterprise Solutions in Faro, Portugal, seeks an AI Enterprise Technical Lead to own enterprise AI delivery on a major global project. You will lead technical discovery, shape architectural strategy across AI agents, RAG workflows, and Azure services, and translate business workflows into scalable AI solutions.

Responsibilities include guiding engineers, aligning with security and governance, and driving continuous improvement of AI capabilities in a complex enterprise environment.

Qualifications

• Strong experience designing and delivering AI/GenAI solutions in enterprise environments.
• Hands-on engineering leadership and delivery of complex AI programs.
• Experience shaping architecture across AI agents, RAG, and orchestration.
• Proven ability to translate business workflows into scalable AI solutions.

Responsabilités

• Lead technical discovery and solution design for enterprise AI projects.
• Shape architecture across AI agents, RAG, and Azure services.
• Provide hands-on guidance and code reviews to delivery teams.
• Ensure governance, security, and observability in enterprise AI solutions.

Connaissances

AI architecture

Technical leadership

Solution design

LLMs

RAG and workflow orchestration

Azure AI services

Outils

Microsoft Azure

Description du poste

WPP Enterprise Solutions designs, builds, and operates the growth systems that competitive businesses rely on. In a world where AI is reshaping how companies drive growth, we lead clients in business transformation and marketing modernization, connecting strategy directly to execution. Our 12,000 experts work within a unified global operating unit across 40+ markets, delivering tailored growth solutions. We are looking for an AI Enterprise Technical Lead to join a high-profile enterprise AI project for a major global brand. This role combines ownership of enterprise AI delivery, client-facing discovery, and technical workshops with strong architectural influence, balancing technical leadership and hands-on engineering.

Lead technical discovery and solution design, translating business workflows into scalable AI solutions. Shape architecture across AI agents, orchestration, RAG/retrieval, Azure services and enterprise integrations. Define clear boundaries between AI/LLM capabilities and deterministic application logic. Design agent workflows, context management, tool usage and appropriate human-in-the-loop controls. Shape RAG and enterprise knowledge solutions, including retrieval and use of trusted sources. Design integrations between AI workflows, APIs and existing enterprise platforms. Ensure appropriate approaches to security, identity and access, governance, observability and auditability. Turn discovery outcomes into architecture, technical scope and an actionable implementation backlog. Lead engineers throughout delivery, providing hands-on guidance, code reviews and support with complex technical decisions. Establish effective approaches to AI evaluation, testing and continuous improvement. Strong commercial experience designing and delivering AI/GenAI solutions in enterprise environments. Strong understanding of LLMs, agentic architectures, RAG and workflow orchestration. Hands-on experience building AI solutions and integrating them with application services and APIs. Good practical knowledge of Microsoft Azure and Azure AI services. Strong understanding of software architecture, API design and enterprise integration patterns. Experience combining LLM-based capabilities with deterministic application logic. Practical understanding of context engineering, retrieval, structured model outputs and AI evaluation. Understanding of security, identity and access, governance and human oversight in enterprise AI solutions. Proven experience leading technical discovery and solution design. Experience providing technical leadership while remaining hands-on when needed. Preferred: Experience designing multi-agent or agentic workflow architectures. Preferred: Experience building enterprise RAG and knowledge retrieval solutions. Preferred: Experience with LLMOps/MLOps and systematic AI evaluation. Preferred: Experience implementing AI guardrails or model routing. Preferred: Experience with infrastructure as code and automated Azure provisioning. Preferred: Relevant Microsoft Azure or AI certifications.