About the Role
We're looking for an MLOps Engineer to join Moonpig's Data Platform team. In this role, you'll help build and scale the infrastructure that powers machine learning across the business. Working closely with data scientists, data engineers, software engineers, and stakeholders, you'll streamline the end-to-end machine learning lifecycle—from experimentation and model development through to deployment, monitoring, and continuous improvement.
As part of the ML Ops team, you'll play a key role in enabling innovation, personalisation, and data-driven decision-making across Moonpig. This is an opportunity to work with modern cloud technologies, shape scalable ML platforms, and make a direct impact on how machine learning is delivered in production.
Key Responsibilities
- Evaluate, integrate, and implement MLOps tools and frameworks to improve the efficiency and reliability of machine learning operations.
- Design, implement, and manage CI/CD pipelines for deploying machine learning models into production environments.
- Build and maintain infrastructure supporting data pipelines, model training, and model serving using cloud-native technologies and infrastructure-as-code practices.
- Optimise machine learning workflows for performance, scalability, resource utilisation, distributed processing, and GPU acceleration.
- Implement monitoring solutions to track model performance, identify anomalies, and support automated retraining processes.
- Develop automated workflows for model testing, validation, and deployment, integrating with CI/CD tooling.
- Partner with data scientists, data engineers, and software engineers to streamline the journey from experimentation to production.
- Ensure security best practices are followed, including access control, data privacy, and compliance requirements.
- Contribute to the ongoing evolution of the data platform, identifying opportunities to improve productivity, reliability, and scalability.
- Build strong relationships across teams and support the adoption of data and machine learning best practices.
About You
- Strong experience writing clean, maintainable, and production-ready Python code.
- Proven ability to build scalable applications, data workflows, and automated solutions.
- Experience working with machine learning pipelines and platforms such as AWS SageMaker or similar technologies.
- Strong understanding of cloud-native services and experience designing, deploying, and operating applications within AWS or comparable cloud environments.
- Comfortable working in agile environments, balancing technical quality with pragmatic delivery.
- Curiosity and enthusiasm for learning new technologies and improving engineering practices.
- Ability to collaborate effectively with a range of technical and non-technical stakeholders.
- Strong problem-solving skills and a focus on building reliable, scalable solutions.
Our Tech Environment
- MLOps: Snowflake, SQL, Python, FastAPI, Metaplane, Grafana, GitHub Workflows.
- Infrastructure: AWS (SageMaker, ECS, Lambda, Glue, S3), Terraform, API Gateway.
- Collaboration: GitHub, Jira, Confluence.
We don't expect you to have experience with every technology listed above. We're interested in engineers who are excited to learn, collaborate, and help us build scalable machine learning platforms that support the future of Moonpig.
How We Get There
- Stage 1: Recruiter Screening Call
- Stage 2: Hiring Manager Interview
- Stage 3: Technical Assessment Interview with Two Team Members
- Offer! 🎉
At Moonpig, we believe great products are built by great teams. You'll work in a collaborative environment where learning is encouraged, ideas are welcomed, and engineering excellence matters. We value people who are curious, supportive, and motivated to make a meaningful impact through technology.
Interview Process
Our process may vary depending on role and availability. We keep candidates informed of any changes.
Skills Required
- Production-ready Python development experience
- Proven ability to build scalable applications, data workflows, and automation
- Experience with machine learning pipelines/platforms such as AWS SageMaker
- Designing, deploying and operating applications within AWS or comparable cloud environments
- Designing and managing CI/CD pipelines for ML model deployment
- Infrastructure-as-code experience (Terraform) and cloud-native infrastructure management
- Implementing monitoring for model performance, anomaly detection, and automated retraining
- Optimising ML workflows for scalability, distributed processing and GPU acceleration
- Ability to collaborate with data scientists, data engineers and software engineers
- Familiarity with tech stack: Snowflake, SQL, FastAPI, Metaplane, Grafana, GitHub Workflows
What We Do
At Moonpig Group our mission is to help people connect and create moments that matter. We’re an international group made up of two brilliant brands – Moonpig in the UK, US and Australia, and Greetz in the Netherlands. We’re a technology platform at heart, but our customers know us as the leading eCommerce destination for greetings cards, gifts and flowers. Last year we delivered over 70 million personalised cards, gifts and flower bouquets in over 50 million orders, helping our customers celebrate all the occasions that matter to them, from milestone birthdays and anniversaries to new arrivals and all of those just-becauses. We have awesome people and a caring company culture: We give teams autonomy while supporting personal growth at all levels. Plus, we know how to have fun! Don’t just take our word for it, though; in Feb 2022, Moonpig was officially recognised as an outstanding company to work for by Best Companies and we earned a 2-Star accreditation, which is Best Companies second-highest standard of workplace engagement and represents organizations striving for the top. Head over to our careers site for more company info and our current opportunities - https://www.moonpig.com/uk/blog/moonpig-careers/moonpig-careers/
