ML Ops Engineer, Moonpig
Online company for personalised cards, gifts and flowers
Job no longer available
Online company for personalised cards, gifts and flowers
501-1000 employees
Job no longer available
501-1000 employees
Company mission
To help people connect and create moments that matter.
Role
Who you are
- Experienced in building cloud/serverless applications, using Python, on AWS
- Have experience working with Machine Learning tools, building pipelines to productionise ML model deployment
- Experience with CI/CD
- Enjoy being part of an engineering team, working closely with people of different specialisms across the business
- Are willing to challenge your own ideas; to try, fail, learn and repeat. You encourage others to do the same
- Prefer a collaborative environment, sharing knowledge through collaboration, pair programming and constructive code review
- Have an awareness of cloud security and work effectively with our internal security team
What the job involves
- Join our innovative team as an MLOps Engineer, where you will play a crucial role in building and maintaining scalable machine learning infrastructure
- You will collaborate closely with data scientists, engineers, and business stakeholders to streamline the end-to-end machine learning lifecycle, from model development to deployment and monitoring
- Your work will ensure the seamless integration of ML models into production systems, leveraging cutting-edge technologies and cloud platforms to optimise performance, automate workflows, and ensure reliability
- This is an exciting opportunity to make a direct impact on the future of data-driven solutions within our organization
- The role sits within the ML Ops team, which is part of the Data Platform Team, and wider Data function
- The position reports into the Engineering Manager for ML Ops and Data Engineering
- Model Deployment and Management: Design, implement, and manage CI/CD pipelines for the deployment of machine learning models into production environments, ensuring scalability, reliability, and performance
- Infrastructure Automation: Build and maintain the infrastructure for data pipelines, model training, and model serving using a cloud platform (AWS) and infrastructure-as-code tools (Terraform)
- Monitoring and Optimization: Implement monitoring systems for models in production to track performance, detect anomalies, and automate model retraining workflows
- Collaboration with Data Teams: Work closely with data scientists, data engineers, and software developers to streamline the end-to-end machine learning lifecycle, from experimentation to productionisation
- Scalability and Performance Tuning: Optimise machine learning workflows for performance and scalability, including resource utilisation, distributed processing, and GPU/TPU acceleration
- Model Versioning and Governance: Implement and manage version control for models and datasets, ensuring compliance with industry best practices for reproducibility and traceability
- Automation of Workflows: Develop automated workflows for model testing, validation, and deployment, integrating with CI/CD tooling
- Security and Compliance: Ensure security best practices are followed, including managing access control, data privacy, and compliance with relevant regulations
- Tooling and Framework Selection: Evaluate, integrate, and implement appropriate ML Ops tools and frameworks to enhance the efficiency of machine learning operations, including experiment tracking (e.g., MLflow) and model serving platforms (e.g., Sagemaker)
- Our Data Tech Environment:
- AWS (inc Sagemaker, EC2, Lambda, Glue, S3, API gateway), Snowflake, Terraform, Python, C#, .NET Core, SQL
- GitHub for SCM, CI/CD through GitHub workflows
- DBT, Google Analytics, GTM, GCP Big Query, Fivetran, Airflow, Metaplane
- Robust and performant cloud/serverless applications
- How we get there:
- Kanban
- Jira / Confluence
- Grafana and AWS Cloudwatch
- Clean Architecture
- TDD
- Pair Programming
- Focus on experimentation to validate our hypothesis
Salary benchmarks
Our take
When Moonpig was founded in 2000, it bought into the idea that a personalised gift is always more meaningful, whether sent from the sofa, the commute, or the gym. Customers quickly bought into this vision, and Moonpig saw rapid growth culminating in its 2011 acquisition by Photobox for ÂŁ120M.
Since then, Moonpig has seen continued success. Through expansion into new products such as flowers (Moonpig is now a top-3 online flower seller) and the development of its own app, Moonpig has established its place within the market for personalised gifts.
And it doesn't plan on slowing down anytime soon, as it looks to expand even further. Recent innovations have included the introduction of giant cards, and the company has also secured the ability to create exclusively branded ranges alongside Disney and Warner Bros. This commitment to innovation positions Moonpig to capture even more market share and strengthen its leadership in the industry.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Company benefits
- Annual reviews
- Attractive annual bonus scheme - with increased earning potential based on business and individual performance
- Pension scheme where we match up to 5%
- Referral scheme with cash bonus available
- Interest-free travel loan
- Sharesave Plan - Save as you earn
- Generous staff Moonpig, Red Letter Days and Buyagift products
- Private medical insurance
- Private dental plan
- Life assurance paying 4x your salary
- Eye care vouchers
- Cycle to work scheme
- Free access to 24h mental health and wellbeing resource as well as one to one therapy sessions via Spill
- Dog-friendly offices
- Access to a broad range of training workshops and courses
- Technical and professional qualifications policy, allowing individuals to apply for a more formal professional qualification, using allocated individual allowance
- Career Conversations with your manager to create your Career Development Plan
- 25 days’ holiday plus 1 day per year of service (up to 30 days)
- Growing Your Family - 8 weeks paid supporting caregiver leave and 24 weeks full pay primary caregiver leave (based on qualifying criteria)
- Flexible working opportunities based on our Ways of Working - inclusive of Hybrid and Remote
- Summer Hours - We finish at 4pm on Friday's during the months of June, July and August
- Volunteering - 1 day - the equivalent of 8 hrs of time to give back - through a chosen charity or our MP Group Foundation
- Matched Funding Scheme - This scheme provides colleagues with a fundraising allowance to support causes close to their hearts
Company HQ
Farringdon, London, UK
Leadership
Catherine Faiers
(CEO)Previously Non-Exec Director at Allegro and COO of Auto Trader and Addison Lee. Was also Corporate Development Director at Trainline and a Director at DC Advisory.
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