Middle ML / MLOps Engineer

We are looking for an experienced Middle ML / MLOps Engineer (3+ years of experience) to join an international project! In this role, you will design, build, and deploy production-scale machine learning and Generative AI / LLM-based applications in a cloud-native environment. 📌 Workload: Full-time (100% Remote) 🗣 Language: English (Fluent — daily technical communication) 💻 Tech Stack: Python, AWS (SageMaker), Docker, Kubernetes, PySpark, FastAPI/Flask, MLOps (MLflow / Kubeflow), LLMs 🌍 Location: Remote within EU (must hold EU Citizenship, PR, or a valid EU Work/Residence Permit) 🎯 Key Responsibilities: * Design, develop, and deploy production-grade ML and LLM-based applications. * Build and maintain robust MLOps platforms and CI/CD pipelines for machine learning workflows. * Automate model training, evaluation, deployment, monitoring, and continuous improvement. * Develop cloud-native ML infrastructure using AWS, SageMaker, Docker, and Kubernetes. * Build reliable backend APIs and microservices using FastAPI or Flask. * Collaborate closely with product, software engineering, and data teams. 🛠 Requirements: * 3+ years of commercial experience as an ML Engineer, MLOps Engineer, or in a related field. * Strong hands-on experience with Python and developing production-ready services with FastAPI / Flask. * Proven experience with AWS (specifically Amazon SageMaker). * Hands-on experience with Docker and Kubernetes for deployment and orchestration. * Solid understanding of MLOps practices and ML lifecycle tools (MLflow, Kubeflow, or SageMaker Pipelines). * Practical experience working with LLMs / Generative AI in production environments. * Hands-on experience with PySpark / Apache Spark. * Fluent English (B2+/C1) for effective collaboration with cross-functional teams. ⭐️ Nice to Have: * Experience with recommendation systems, NLP, or forecasting use cases. * Knowledge of model monitoring and observability tools.