MLOps Engineer

Updraft is an award winning, FCA-authorised, high-growth fintech based in London. Our vision is to revolutionise the way people spend and think about money, by automating the day to day decisions involved in managing money and mainstream borrowings like credit cards, overdrafts and other loans.

• A 360 degree spending view across all your financial accounts (using Open banking)
• A free credit report with tips and guidance to help improve your credit score
• Native AI led personalised financial planning to help users manage money, pay off their debts and improve their credit scores.
• Intelligent lending products to help reduce cost of credit

We have built scale and are getting well recognised in the UK fintech ecosystem.

• 800k+ users of the mobile app that has helped users swap c £500 m of costly credit-card debt for smarter credit, putting hundreds of thousands on a path to better financial health
• The product is highly rated by our customers. We are rated 4.8 on Trustpilot, 4.8 on the Play Store, and 4.4 on the iOS Store.
• We are selected for Technation Future Fifty 2025 – a program that recognizes and supports successful and innovative scaleups to IPOs - 30% of UK unicorns have come out of this program.
• Updraft once again featured on the Sifted 100 UK startups - among only 25 companies to have made the list over both years 2024 and 2025.

We are looking for exceptional talent to join us on our next stage of growth with a compelling proposition - purpose you can feel, impact you can measure, and ownership you'll actually hold. Expect a hybrid, London-hub culture where cross-functional squads tackle real-world problems with cutting-edge tech; generous learning budgets and wellness benefits; and the freedom to experiment, ship, and see your work reflected in customers' financial freedom. At Updraft, you'll help build a fairer credit system.

The Role

We're looking for an experienced MLOps Engineer for a 3-month contract to lead the development of our ML deployment, testing, monitoring, and feature engineering pipelines. You'll be responsible for establishing best practices and production-grade systems to support our machine learning workflows from training to deployment and beyond. The role could be extended to a longer DevOps contract.

- Design and build an end-to-end MLOps pipeline using AWS, with a strong focus on SageMaker for training, deployment, and hosting.

- Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility.

- Architect and implement a feature store strategy for consistent, discoverable, and reusable features across training and inference environments (e.g., using SageMaker Feature Store, Feast, or custom implementation).

- Work closely with data scientists to formalize feature engineering workflows, ensuring traceability, scalability, and maintainability of features.

- Develop unit, integration, and data validation tests for models and features to ensure stability and quality.

- Establish model monitoring and alerting frameworks for real-time and batch inference (e.g., model drift detection, performance degradation).

- Build CI/CD pipelines for ML workflows (training, evaluation, deployment), integrating with tools such as GitHub Actions, CodePipeline, or Jenkins.

- Create internal documentation and onboarding guides for engineering and data teams to adopt new MLOps practices.

Requirements

- 3+ years of experience in MLOps, DevOps, or ML infrastructure roles.

- Deep familiarity with AWS services, especially SageMaker, S3, Lambda, CloudWatch, IAM, and optionally Glue or Athena.

- Strong experience with MLflow, experiment tracking, and model versioning.

- Proven experience setting up and managing a feature store, and driving best practices for feature engineering in production systems.

- Proficiency in model testing strategies, including unit testing for pipelines, data validation (e.g., Great Expectations, Deequ), and A/B or shadow testing.

- Experience with model monitoring frameworks

- Solid knowledge of CI/CD for ML, including automated training and deployment workflows.

- Strong Python engineering skills; experience with Docker and orchestration tools is a plus.

- Excellent communication and documentation skills, and a strong collaborative mindset.

- Experience in startups and/or fintech

- Exposure to data privacy, compliance, or secure model delivery practices.

Benefits

- Direct impact on how ML is deployed and maintained at scale in a high-growth startup.

- A greenfield opportunity to set the standard for ML operations and infrastructure.

- Offices in HSR area, fast iteration cycles, and a culture of ownership.