Lead Engineer - Consumer Intelligence & Engagement

The Lead Engineer will be part of the Consumer Intelligence & Engagement organisation, which delivers the intelligence and engagement capabilities that enable more relevant and personalised consumer experiences across digital channels. The role will provide technical leadership for the team building production-grade backend services that power personalisation and recommendation capabilities, owning technical architecture, engineering standards and key technology decisions. Working closely with the Engineering Manager, Product Owners, and Data Engineering teams, the Lead Engineer will deliver low-latency services that support real-time consumer experiences and will champion engineering best practices, code quality, observability and continuous improvement.

• Lead the design and development of personalisation and recommendation services and applications.
• Own technical architecture, engineering standards, and key technology decisions across model serving, inference, and recommendation platforms.
• Design and build scalable, low-latency APIs, event-driven services, and integration layers that support real-time and batch recommendation use cases.
• Partner with AI/ML Engineers and Data Engineers to deploy machine learning models and recommendation algorithms relevant for the business use case.
• Design and implement model serving, inference, feature access, and experimentation capabilities that support personalisation and recommendation.
• Integrate LLM APIs and AI services to enable intelligent consumer experiences and business capabilities.
• Ensure solutions are secure, observable, reliable, and resilient through strong engineering practices.
• Drive engineering excellence through architecture reviews, code reviews, technical mentorship, and continuous improvement.
• Optimise performance, scalability, reliability, and operational efficiency through automation and proactive improvements.
• Leverage AI-assisted engineering practices and tools to improve engineering productivity, code quality, and delivery effectiveness.
• Provide technical leadership across teams.

• 10+ years of experience designing and building large-scale distributed backend systems on cloud-native platforms.
• 3+ years of experience leading a technical capability, project, or engineering team.
• Hands-on experience building and operating recommendation systems and personalisation capabilities.
• Strong understanding of production ML integration patterns, including model serving, inference, experimentation, feature access, observability, and operationalisation of ML capabilities.
• Experience building model serving and inference integration layers for real-time and batch prediction workloads.
• Experience integrating and operationalising LLM APIs and AI services within production applications.
• Experience with experimentation platforms, A/B testing, and data-driven optimisation of consumer experiences.
• Expertise in backend engineering, API design, microservices, event-driven architectures, and scalable integration patterns using Java, Spring Boot, and related frameworks.
• Experience with Redis or equivalent caching systems.
• Experience building event-driven systems using Kafka or equivalent technologies.
• Experience with SQL and NoSQL databases, database architecture, and distributed data management.
• Strong understanding of data-intensive systems, feature engineering concepts, and high-throughput data processing patterns.
• Experience with observability, reliability engineering, and performance optimisation.
• Proven ability to drive architectural decisions, influence technical direction, and mentor engineers.
• Experience using coding agents, LLMs, or AI-assisted engineering tools to improve software delivery and developer productivity.

• Experience with feature stores, vector databases, embedding-based retrieval, and modern ML architectures is considered a strong advantage.
• Experience in eCommerce, retail, or consumer digital products is strongly preferred.