ML / MLOps Engineer

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• 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.

• 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);
• Nice to have: Experience with recommendation systems, NLP, or forecasting use cases, knowledge of model monitoring and observability tools.

• Remote work within the EU.