Senior MLOps Engineer

Das ist der Job

Do you want to be the engineer who turns pricing science into systems that actually run in production?

Darum lohnt es sich

As a Senior MLOps Engineer on the Dynamic Pricing & Revenue Management team, you'll work alongside a Data Scientist and a Data Analyst to build the infrastructure that helps the business price smarter and drive real revenue impact, at genuine scale.

Own Infrastructure and Tooling: Set up and maintain the environments, CI/CD pipelines, and infrastructure the team depends on. Drive Operational Excellence: Implement monitoring, automated testing, and observability across the team's production systems.

The Team: You’ll join a small, newly formed team inside a larger engineering organisation, with a clear mission: help drive smarter, data‑driven pricing decisions across the business. It’s a team that cares about impact, collaboration, and learning together, using modern tooling day to day.

What They’re Looking For: 4+ years of experience in Software Engineering, Data Engineering, DevOps, or MLOps Strong hands‑on Python, writing clean, production‑quality code Experience with CI/CD, Docker, and infrastructure‑as‑code such as Terraform Familiarity with cloud platforms, AWS preferred, and deploying services into production Exposure to or interest in ML model deployment, tools like MLflow or SageMaker are a strong plus A proactive, hands‑on mindset, someone who takes ownership, spots problems, and drives solutions forward Comfortable using modern AI‑assisted development tools as part of your workflow What They Offer: Competitive senior‑level package and: Hybrid working with flexible in‑office collaboration time Flexibility to work from other locations internationally for part of the year Personal learning budget Gym discounts and travel perks Join an international team, working on products used at real scale #J-18808-Ljbffr What You’ll Do: Bridge Data Science and Production: Help deploy pricing and demand models into production, building and maintaining the APIs and serving infrastructure underneath them.

Build and Operate Pipelines: Ensure data flows reliably from source to model to output, with proper monitoring and alerting built in from the start. Work Cross-Functionally: Partner closely with Data Scientists, Analysts, and Engineering to turn prototypes into robust, production-ready Python applications.