Manager, Technical Solutions

WPP is the trusted growth partner for the world’s leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships - all united by ourpioneeringagentic marketing platform, WPP Open - we help clients navigate change, capture opportunity, and deliver transformational growth.

WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation nected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working.

We are seeking a highly skilled and passionate Manager to join our cutting-edge AI Automation team. In this role, you will be instrumental in designing, building, and deploying robust, scalable, and high-performance AI/ML solutions that automate and optimize critical media processes across WPP's global network.

You will work closely with Data Scientists, Business Analysts, and Product Managers to translate complex business problems and ML models into production-ready software, focusing on MLOps best practices, system integration, and cloud-native development. If you thrive on solving challenging technical problems, have a strong engineering mindset, and are eager to make a significant impact on the future of media, this is the role for you.

Key Responsibilities:

• Software Development & Architecture:
• Design, develop, test, deploy, and maintain high-quality, scalable, and resilient software applications and microservices for AI/ML automation initiatives.
• Lead technical design discussions, propose architectural solutions, and ensure adherence to best practices in coding, security, and performance.
• Write clean, maintainable, and well-documented code primarily in Python, with potential for other languages (e.g., Java, Go) for specific services.
• Conduct thorough code reviews, provide constructive feedback, and ensure code quality standards are met.
• AI/ML System Integration & MLOps:
• Operationalize machine learning models developed by Data Scientists, integrating them into production systems via APIs, batch processes, or streaming pipelines.
• Implement and manage MLOps pipelines for model training, validation, deployment, monitoring, and retraining (e.g., using tools like MLflow, Kubeflow, Sagemaker, Azure ML, Vertex AI).
• Develop robust data pipelines for feature engineering, data ingestion, transformation, and storage to support AI models.
• Design and implement systems for real-time inference and prediction serving at scale.
• Cloud-Native Development:
• Develop and deploy applications leveraging cloud platforms (AWS, Azure, GCP), utilizing services such as serverless functions, container orchestration (Kubernetes, ECS/AKS/GKE), managed databases, and message queues.
• Implement CI/CD pipelines to automate the build, test, and deployment processes.
• Monitor and optimize cloud resource utilization and application performance.
• Work closely with Business Analysts to understand requirements and translate them into technical specifications.
• Collaborate with Data Scientists to refine model interfaces, optimize performance, and troubleshoot production issues.