Senior MLOps Engineer at ASOS

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

01 Feb, 26

Salary

0.0

Posted On

03 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, MLOps, Python, CI/CD, Model Registries, Containerisation, Cloud Platforms, Distributed Computing, Automation, Standardisation, Tooling, Monitoring, Testing, Feature Management, Data Engineering, Platform Engineering

Industry

Retail Apparel and Fashion

Description

Company Description We’re ASOS, the online retailer for fashion lovers all around the world. We exist to give our customers the confidence to be whoever they want to be, and that goes for our people too. At ASOS, you’re free to be your true self without judgement, and channel your creativity into a platform used by millions. But how are we showing up? We’re proud members of Inclusive Companies, are Disability Confident Committed and have signed the Business in the Community Race at Work Charter and we placed 8th in the Inclusive Top 50 Companies Employer list. Everyone needs some help showing up as their best self. Let our Talent team know if you need any adjustments throughout the process in whatever way works best for you. Job Description As a Senior Machine Learning Engineer, you’ll focus on designing and implementing reusable ML templates, deployment patterns, and MLOps tooling that support scalable, reliable, and secure ML solutions across the organisation. You’ll collaborate closely with ML Engineers and Scientists embedded in product teams such as Forecasting, Recommendations, Marketing, Customer, and Pricing helping them accelerate delivery and improve the quality of ML systems by providing a robust and standardised ML development experience. What you’ll be doing: Designing and developing shared ML engineering templates, tooling, and infrastructure to support ML teams across ASOS. Driving standardisation and reusability of ML workflows, enabling consistency across diverse product domains. Enabling teams to productionise ML models efficiently by providing best practices, templates, and technical support. Implementing and promoting ML Ops principles — including CI/CD for ML, model registries, monitoring, testing, and feature management. Collaborating with ML teams to understand pain points and evolve the platform accordingly. Partnering with Data Engineering, Platform Engineering, and Security teams to ensure scalable and cost-efficient ML infrastructure. We believe being together in person helps us move faster, connect more deeply, and achieve more as a team. That’s why our approach to working together includes spending at least 2 days a week in the office. It’s a rhythm that speeds up decision-making, helps ASOSers learn from each other more quickly, and builds the kind of culture where people can grow, create, and succeed. Qualifications About You Professional experience as a Machine Learning Engineer, ideally with exposure to platform or infrastructure-focused work. Solid understanding of the end-to-end ML lifecycle, from experimentation through deployment and monitoring. Proficiency in Python and familiarity with ML libraries like scikit-learn, XGBoost, PyTorch or TensorFlow. Experience with ML Ops tools and practices such as MLflow, model registries, containerisation (Docker/Kubernetes), and CI/CD pipelines. Comfortable working with cloud platforms (preferably Azure) and distributed computing environments (e.g., Spark, Databricks). Passionate about improving developer experience through automation, standardisation, and tooling. Additional Information BeneFITS’ Employee discount (hello ASOS discount!) ASOS Develops (personal development opportunities across the business) Employee sample sales Access to a huge range of LinkedIn learning materials 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

Responsibilities

Design and implement reusable ML templates and MLOps tooling to support scalable ML solutions. Collaborate with ML teams to standardize workflows and improve the quality of ML systems.