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ML Ops Engineer
Moonpig
London Area, UAE
Full Time
Mid
Hybrid
3 weeks ago
PythonAWS SageMakerAWSCI/CDInfrastructure as CodeTerraform
Free
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PythonAWS SageMakerAWS
About the Role
Moonpig is seeking an MLOps Engineer to join the Data Platform team, building and scaling infrastructure for machine learning across the business. You will streamline the ML lifecycle from experimentation to deployment, working with data scientists and engineers.
Key Skills for This Role
PythonAWS SageMakerAWSCI/CDInfrastructure as CodeTerraform
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
Requirements
- 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
- 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
Full Job Posting
About The Role
- We're looking for an MLOps Engineer to join Moonpig's Data Platform team.
- You'll help build and scale the infrastructure that powers machine learning across the business.
- Work closely with data scientists, data engineers, software engineers, and stakeholders to streamline the end to end machine learning lifecycle.
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.
What's in it for you?
- Competitive Pay & Bonuses: Plus, generous pension plans & staff discounts.
- Wellbeing First: Private healthcare (UK), mental health support & dog friendly offices (London & NL).
- Flexible Working & Time Off: Generous holidays, hybrid working (1 3 days in office, depending on role/team) & up to 20 days of international working.
- Career Growth: Learning allowances, coaching & development programs.
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