Machine learning engineer in search and recommendations

Описание:

At ASOS, we're building the next generation of AI-powered Search and Discovery experiences for millions of customers worldwide. Our Search & Recommendations team develops the platforms, infrastructure and machine learning systems that power personalised product discovery, ranking, retrieval and recommendation experiences across ASOS. We operate high-scale production systems that enable data scientists and ML engineers to rapidly develop, deploy and monitor machine learning solutions in a reliable, scalable and cost-effective way.

Задачи:

• Design and build scalable machine learning platforms and infrastructure supporting model training, deployment and serving.
• Develop highly available backend services that power recommendation, search and personalisation experiences for millions of customers.
• Build and maintain CI/CD pipelines for machine learning and data products.
• Design batch and real-time inference architectures using modern cloud-native technologies.
• Improve reliability, resilience and performance across ML workloads through monitoring, observability and automation.
• Build tooling and frameworks that enable data scientists and ML engineers to deploy models safely and efficiently.
• Own production services, infrastructure and operational excellence practices, including incident management and root cause analysis.
• Optimise distributed compute workloads and resource utilisation across cloud environments.
• Drive Infrastructure-as-Code adoption and platform standardisation across machine learning systems.
• Contribute to architectural decisions across recommendation, search and AI platforms.
• Mentor engineers and promote software engineering best practices across the organisation.
• Help shape ASOS's long-term machine learning platform strategy.

Требования:

• Strong software engineering fundamentals with experience designing and building production systems at scale.
• Experience developing distributed systems, microservices or high-throughput backend platforms.
• Strong programming skills in Python, Java, Kotlin, Go, Scala or similar languages.
• Experience building and operating services in AWS, Azure or GCP environments.
• Hands-on experience with Kubernetes, containerisation and cloud-native technologies.
• Experience implementing CI/CD pipelines and automated deployment processes.
• Knowledge of Infrastructure-as-Code tools such as Terraform, Pulumi or CloudFormation.
• Experience with monitoring, alerting and observability tooling.
• Experience building reliable, resilient and scalable systems with a focus on performance and operational excellence.
• Experience working with data-intensive systems, streaming technologies or large-scale distributed processing platforms.
• Exposure to machine learning systems, model serving, feature stores, training infrastructure or MLOps practices.
• Comfortable providing technical leadership, mentoring engineers and influencing architectural direction.
• Strong collaboration and communication skills, with experience working in cross-functional product teams.
• Experience supporting large-scale model training and inference workloads.
• Будет плюсом: Experience supporting recommendation systems, search platforms, personalisation engines or other customer-facing data products is advantageous; Knowledge of vector search, ranking systems, retrieval architectures or recommendation platforms; Exposure to LLMs, Generative AI and production AI systems; Experience building internal developer platforms, engineering enablement tooling or shared capabilities used across multiple teams

Условия:

• Employee discount (hello ASOS discount!)
• Employee sample sales 25 days paid annual leave + an extra celebration day for a special moment
• Discretionary bonus scheme
• Private medical care scheme
• Flexible benefits allowance - which you can choose to take as extra cash, or use towards other benefits
• Opportunity for personalised learning and in-the-moment experiences that enable you to thrive and excel in your role

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