MLOps Engineer

MLOPS ENGINEER (MID-WEIGHT OR SENIOR) | FINTECH SCALEUP | LONDON (HYBRID) The Opportunity: Join a growing data team in a FinTech Scaleup, now led by a newly appointed Head of Data, focused on expanding the machine learning function. This is an exciting opportunity to be the first MLOps Engineer in a 14-person data team, working closely with the Data Science Manager to shape the future of ML operations. Location: Hybrid Working in London (x3 a week in the office) Salary: £65K - £95K per annum depending on level of experience, plus 20% discretionary annual bonus Key Projects: Develop machine learning models to predict merchant payment behaviour and ensure smooth, production-level deployment. Create efficient, scalable processes to train, deploy, and monitor models in a production environment. Key Responsibilities: Architect efficient processes to operationalize and maintain ML models. Design, implement, and optimize scalable ML pipelines, covering everything from model training to deployment and monitoring. Build robust data infrastructure to support simulations and production usage. Set up systems to monitor model performance in production environments. Automate the ML lifecycle, focusing on scalability and efficiency. Collaborate with data scientists and team members to integrate the latest advancements in machine learning into production. Required Skills: Proven experience in building and maintaining operational ML systems. Proficiency in Python and familiarity with ML/Data Engineering libraries. Strong understanding of DevOps practices such as CI/CD, containerization (Docker, Kubernetes), and version control (Git). Ability to troubleshoot complex ML pipelines and address potential performance bottlenecks. Tech Stack: Terraform AWS Rackspace Helmchart Jenkins Snowflake Nice to Have: Experience with the tech stack listed above Experience working in FinTech startups or scaleups. This role offers the chance to be at the forefront of developing an ML infrastructure that will power key business decisions. If you're passionate about operationalizing machine learning models and thrive in dynamic startup/scaleup environments, please apply to this advert or email your CV to [email protected].