(Senior) Machine Learning Platform/Ops Engineer, Remote/Europe

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a (Senior) Machine Learning Platform/Ops Engineer in Europe.As a (Senior) Machine Learning Platform/Ops Engineer, you will be instrumental in building and maintaining the infrastructure that powers production-ready ML models at scale. You will collaborate closely with data scientists and software engineers to design reproducible pipelines, operationalize models, and ensure high availability and performance across batch and real-time workflows. This role provides the opportunity to drive innovation in ML operations, standardize best practices, and accelerate experimentation, all within a highly collaborative and forward-thinking environment. You will contribute directly to the development of robust, scalable ML systems, fostering reproducibility and reliability while mentoring team members. Working remotely across Europe, you will shape the ML infrastructure strategy and enable teams to bring advanced AI models safely and efficiently into production.Accountabilities:Design, implement, and maintain scalable, containerized ML pipelines for model training, evaluation, and deployment across batch and real-time workflowsOperationalize models at scale, building and managing ML services, APIs, and serving infrastructure using tools such as MLflow, SageMaker, and Feature StoreSet up automated monitoring, observability, and alerting systems to ensure high availability, detect model/data drift, and maintain inference performanceDevelop internal libraries, templates, and platform tooling to improve reproducibility and simplify deployment workflows for all ML teamsImplement CI/CD pipelines for model and data workflows using Docker, Terraform, Jenkins, or equivalent, and promote best practices across the organizationEvaluate and adopt emerging MLOps technologies to enhance efficiency, scalability, and reliability of ML platformsMentor junior engineers and foster cross-functional collaboration between engineering and data science teamsRequirements2+ years of hands-on experience operationalizing, deploying, monitoring, and maintaining ML models at scaleStrong proficiency with Python and ML libraries such as scikit-learn, LightGBM, PyTorch, or TensorFlow; solid SQL skillsExperience with infrastructure-as-code and CI/CD systems (Docker, Terraform, Jenkins, or equivalent) and at least one major cloud provider (AWS, GCP, or Azure)Knowledge of ML monitoring and logging, including model drift detection, data validation, and performance/feature trackingExcellent collaboration and communication skills, able to partner effectively across engineering and data science teamsBonus: Experience with feature stores, model versioning, or building internal ML platformsBenefitsFlexible remote work up to 5 days per week across EuropeInternational, collaborative teams with opportunities to contribute ideas directly to platform developmentEducational budget to support continuous learning and skill developmentParticipation in team events, including Hackathons, to foster collaboration and innovationDiscounts on partner platforms for high-quality used carsPersonal growth opportunities in a dynamic, fast-moving environmentJobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching. When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.📊 It compares your profile to the job's core requirements and past success factors to determine your match score.🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.Thank you for your interest!