Cloud MLOps Engineer

Contract details

• Duration : ASAP until end of year, with extensions
• Language : Fluency in English
• Work setup : 50% onsite / 50% remote
• Process : 1 Stage Interview

For a banking client , we are looking for a Cloud (ML)Ops Engineer to work at the intersection of cloud infrastructure, DevOps, and machine learning operations. You will help build and maintain a scalable, secure, and reliable platform supporting data scientists and analysts across their full workflow.

Key responsibilities

• Design and build cloud-native platform services for AI models and data pipelines
• Support multi-user Jupyter environments and cloud IDEs
• Enable training, storage, serving, and monitoring of custom models (mainly high-throughput batch processing)
• Expose models via APIs for low-latency use cases
• Support Generative AI initiatives
• Manage infrastructure on AWS using Terraform, Docker, and Kubernetes
• Automate data and model lifecycle workflows (Airflow, Spark, Python)
• Ensure platform reliability, performance, and cost efficiency
• Support onboarding, troubleshooting, and continuous improvement of MLOps practices
• Collaborate with stakeholders across multiple locations and countries

Requirements

• Strong interest in cloud, data, and AI
• Master’s degree in ICT, Engineering Sciences, Business Engineering (informatics focus), or equivalent experience
• Strong Python skills and familiarity with the data science ecosystem
• Experience with AWS cloud infrastructure
• Docker & Kubernetes knowledge
• Infrastructure as Code (Terraform)
• CI / CD experience (e.g. Jenkins, GitHub Actions)
• Experience with big data tools such as Spark