Lead AI Engineer

Role Overview 

Lead AI Engineer IS to drive the next generation of AI capabilities across WPP proprietary creator, influencer, and social commerce platforms. 

This role combines hands-on AI engineering, product innovation, and technical leadership. The successful candidate will lead the development and deployment of practical AI solutions across influencer intelligence, creator workflows, social commerce, automation, and large-scale content and data analysis. 

The ideal candidate combines strong engineering capability with product thinking and the ability to rapidly translate ideas into scalable AI features. This role is highly execution-focused and suited for someone who enjoys building production-ready AI systems, experimenting with emerging technologies, and solving real business problems through AI. 

You will work closely with product, engineering, strategy, and regional market teams to shape and deploy AI-powered features used across APAC and global client initiatives. 

 

Core Responsibilities 

AI Product Development & Integration 

• Design, develop, and deploy AI-powered features across WPP proprietary platforms.  

• Build practical AI solutions leveraging LLMs, multimodal AI, embeddings, vector search, intelligent workflow orchestration, and AI agents.  

• Integrate AI capabilities into influencer planning, creator discovery, content analysis, workflow automation, reporting, and operational tools.  

• Collaborate with product and engineering teams to translate business requirements into scalable AI solutions.  

• Rapidly prototype, test, and iterate AI features for production deployment.  

 

Generative AI & LLM Engineering 

• Build and optimize applications using modern AI ecosystems such as OpenAI, Gemini, Claude, Vertex AI, and related frameworks.  

• Develop prompt engineering, retrieval-augmented generation (RAG), semantic search, embeddings pipelines, and AI orchestration workflows.  

• Improve AI output quality, evaluation frameworks, guardrails, and model performance for production use cases.  

• Explore and implement conversational AI, recommendation systems, and intelligent automation solutions.  

• Evaluate emerging AI tools, frameworks, and models to identify practical business applications.  

 

AI Infrastructure & Deployment 

• Develop scalable AI services and APIs for internal platforms and client-facing products.  

• Support deployment, monitoring, optimization, and maintenance of AI systems in production environments.  

• Work closely with engineering teams on architecture, cloud deployment, scalability, and security.  

• Contribute to MLOps / AIOps workflows, CI/CD pipelines, and model deployment best practices.  

• Support integration of AI services into existing product ecosystems and workflows.  

Data & Platform Integration 

• Work with structured and unstructured datasets including social, creator, video, image, commerce, and campaign data.  

• Integrate external APIs, data providers, and AI services into internal platforms.  

• Support development of scalable data pipelines and AI-ready infrastructure.  

• Collaborate with product and analytics teams to improve data quality, accessibility, and AI usability.  

• Contribute to the design and validation of AI-ready data lake architecture to ensure it supports scalable AI pipelines, analytics, and downstream product use cases. 

Innovation & R&D 

• Continuously evaluate emerging AI technologies, tools, and frameworks.  

• Prototype and validate new AI concepts rapidly.  

• Help shape AI roadmap and identify high-impact opportunities across products and operations.  

• Stay current on developments in generative AI, multimodal AI, automation, and applied machine learning.  

• Contribute ideas that improve product differentiation and operational efficiency.  

 

Team Leadership & Collaboration 

• Mentor and support junior AI engineers and technical team members.  

• Help establish engineering best practices, documentation standards, and scalable development workflows.  

• Collaborate cross-functionally with product managers, developers, strategists, and regional stakeholders.  

• Communicate technical concepts clearly to both technical and non-technical audiences.  

• Contribute to a culture of experimentation, innovation, and continuous learning.  

 

Desired Skills & Experience 

Technical Skills 

• Strong proficiency in Python and modern AI development frameworks.  

• Hands-on experience with LLMs, generative AI systems, embeddings, vector databases, and RAG architectures.  

• Experience with OpenAI, Gemini, Claude, Vertex AI, LangChain, or similar AI ecosystems.  

• Experience working with APIs, vector databases, embeddings pipelines, and modern AI orchestration frameworks.  

• Familiarity with multimodal AI including image, video, and text analysis.  

• Experience building APIs, backend services, and production-ready AI applications.  

• Experience with Docker, CI/CD workflows, and scalable deployment practices.  

• Familiarity with SQL, NoSQL, and large-scale data processing workflows.  

• Experience contributing to data lake and big data architecture design, with the ability to help define and validate AI-ready data infrastructure that supports scalable AI pipelines, embeddings, semantic search, analytics, and downstream reporting. 

• Experience using AI-assisted development tools to improve coding, debugging, testing, documentation, and engineering productivity. 

• Understanding of cloud platforms such as GCP or AWS. 

 

Experience 

• 5+ years in software engineering, machine learning, or applied AI roles, with recent hands-on experience in generative AI and LLM-based applications.  

• Proven experience building and deploying AI-powered products or features in production environments.  

• Experience leading projects or mentoring junior technical resources.  

• Strong product mindset with the ability to balance experimentation and execution.  

• Comfortable operating in fast-paced product or startup-like environments.  

 

Preferred Experience 

• Experience with social media, creator economy, influencer marketing, recommendation systems, or commerce platforms.  

• Experience with semantic search, content intelligence, or automation workflows.  

• Exposure to AI agents and workflow orchestration systems.  

• Experience integrating AI into enterprise or SaaS platforms.  

• Familiarity with analytics, creator intelligence, or content recommendation products is a plus.