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Moonpig

Posted 4 days ago

Open

ML Ops Engineer

LondonHybrid

AI Summary

We’re the Moonpig Group – home to Moonpig, Greetz, Red Letter Days and Buyagift – and we’re on a mission to make people feel loved, celebrated and remembered. Whether it’s a card that gets them laughing out loud or a gift that makes their day, we help people stay close, no matter the miles.We’re proud to be leading the online gifting revolution, with brilliant products, clever tech and a whole lot of heart.

About this role

We’re the Moonpig Group – home to Moonpig, Greetz, Red Letter Days and Buyagift – and we’re on a mission to make people feel loved, celebrated and remembered. Whether it’s a card that gets them laughing out loud or a gift that makes their day, we help people stay close, no matter the miles.

We’re proud to be leading the online gifting revolution, with brilliant products, clever tech and a whole lot of heart. Our platform makes it easy to create moments that matter – packed with personal touches and delivered with care.

We’re not just about selling cards or gifts – we’re here to spread joy, spark smiles and make every celebration feel extra special. And with values that guide how we work and support one another, we’ve built a place where people (and ideas) can truly thrive.

If you’re looking to make an impact, bring your spark and be part of something meaningful – we’d love to have you on the team. 🌙🐷

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

    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

    • Stage 1: Recruiter Screening Call
    • Stage 2: Hiring Manager Interview
    • Stage 3: Technical Assessment Interview with Two Team Members
    • Offer! 🎉

      • Our process may vary depending on role and availability. We keep candidates informed of any changes.

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