SPEED & SPIRIT is what we look for in our candidates, defined by some simple values that inspire us to BE DRIVEN in our performance, BE VIBRANT in our sporting legacy, BE TOGETHER in our team spirit, and to let our individual talent and experience shine. Applying for a job at PUMA is easy.
Simply click APPLY ONLINE and follow the steps to upload your application.
YOUR MISSION
You will be part of the Consumer Intelligence & Engagement organisation responsible for delivering the intelligence and engagement capabilities that enable more relevant and personalised consumer experiences across digital channels. The organisation plays a key role in how consumers discover products, engage with content, and elevate experiences through membership & loyalty. Through capabilities such as personalisation, recommendations, consumer intelligence, experimentation, and AI-driven decisioning, the organisation helps deliver more contextual, data-driven interactions across the consumer journey.
As a Lead Engineer, you will provide technical leadership for the team building production grade backend services that power personalisation and recommendation capabilities. You will own technical architecture, engineering standards and key technology decisions, ensuring solutions are scalable, reliable, and resilient. Working closely with the Engineering Manager, Product Owners, and Data Engineering teams, you will deliver low latency services that support real-time consumer experiences. You will champion engineering best practices, code quality, observability, and continuous improvement across the team.
What you will do:
- Lead the design and development of personalisation and recommendation services & 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.
YOUR TALENT
- 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, 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 working with feature stores, vector databases, embedding based retrieval and modern ML architectures will be a strong advantage.
- 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 in eCommerce, retail, or consumer digital products is strongly preferred.
PUMA provides equal opportunities for all job applicants, regardless of race, color, religion, national origin, sex,
gender identity or expression, sexual orientation, age, or disability. Equality for all is one of the core principles at
PUMA and we do not tolerate any form of harassment or discrimination.
At PUMA, every application is reviewed by real people who are committed to fairness, transparency, and equal
opportunity - no matter your background, identity, or experience.To ensure our process stays true to these values, no automated systems or AI tools are used to make hiring decisions. Every decision is made by real people -with real judgment and accountability. We may use functions supported by Artificial Intelligence (AI) to carry out isolated organizational steps, such as scheduling interviews. These functions have no influence on decisions in the application process. We believe in creating spaces where everyone is welcome, celebrated, and empowered to contribute authentically. Because at PUMA, whoever wants to play, can play.
PUMA is a global sports brand creating footwear, apparel, and accessories that inspire athletes and everyday movers. The PUMA Group owns PUMA, Cobra Golf, and stichd, operates in 120+ countries, and has around 22,000
employees worldwide.
