ML OPS Engineer - R&D IT

What You'll Do

• [FP1.1][FP1.2][FP1.3][FP1.4][FP1.5] · Stakeholder Collaboration: Work with project managers, resource managers, IT teams, and other stakeholders to gather requirements, define project scope, and ensure alignment with business objectives. · CI/CD, Automation & Developer Experience: Design and maintain automated pipelines and development tooling that streamline the workflow for data scientists and ML engineers.
• Provide standardized environments, reusable templates, and smooth local‑to‑production processes to improve productivity and ensure fast, reliable delivery across ML, analytics, and data engineering projects. · Platform & Infrastructure Engineering: Develop and manage cloud and on‑prem infrastructure supporting data processing, analytics applications, and ML workloads.
• Ensure reliability, scalability, and reproducibility. · MLOps & Model Lifecycle Support: Support both existing ML models already running in production and the development of future AI/ML products.
• Implement and maintain model registries, deployment workflows, monitoring solutions, and automated retraining strategies to ensure reliable, long‑term model operations. · GenAI Platform Enablement: Build and operate infrastructure for Generative AI applications—such as setting up and maintaining MCP servers for internal chatbots and knowledge assistants.
• Support existing GenAI products already in production and ensure they run securely, efficiently, and at scale. · Data & Analytics Pipeline Enablement: Partner with data engineers to enhance data pipelines, ensure data quality, and optimize workflows powering visualizations, dashboards, and ML systems. · Cross‑functional Collaboration: Work with teams across R&D, IT, and product areas to gather requirements, co‑design solutions, and align infrastructure decisions with business needs.
• What you bring[FP2.1] You can describe yourself as follows: Education & Experience • Education: Master’s degree in data engineering, Software Engineering, Computer Science, or a related technical field • Experience: 10+ years of experience as a software, data or DevOps engineer, preferably within a complex IT or R&D environment Technical Skills • Strong proficiency in Python and Bash • Hands‑on experience with containerization (Docker) • Experience implementing monitoring and observability solutions – ideally Splunk, but others are welcome (Prometheus, Grafana, ELK) • Proficiency with Git and experience working with modern version‑control platforms – preferably GitLab • Experience building and maintaining cloud infrastructure, ideally on AWS • Proven experience writing Infrastructure as Code (IaC) using tools such as Terraform or Cloud Development Kit (CDK) Professional Attributes • Strategic Problem-Solving: Comfortable owning technical challenges and designing long-term, scalable solutions. • Customer & Stakeholder Focus: Strong communicator who can translate technical concepts into business value and collaborate effectively across data science, architecture, and wider R&D. • Team Mindset: A natural collaborator who contributes to an open, supportive working culture. • Agile & Scrum: Experienced working in Agile environments, actively participating in sprints, stand-ups, and iterative delivery cycles to ensure continuous improvement and timely value delivery.
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