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Это отличная возможность в самом хайповом сейчас домене. Хотя роль довольно широкая и охватывает MLOps, ML и бэкенд, стек современный, а фокус на Generative AI дает отличный буст для карьеры.
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Описание вакансии
TL;DR
Middle ML / MLOps Engineer (Python/AWS/Generative AI): Designing, building, and deploying production-scale machine learning and LLM-based applications in a cloud-native environment with an accent on MLOps platforms, model lifecycle automation, and reliable backend services. Focus on building ML infrastructure with SageMaker, Docker, and Kubernetes, automating training and monitoring pipelines, and developing production APIs and microservices.
🔥 Hiring: 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.
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📌 Workload: Full-time (100% Remote)
🗣 Language: English (Fluent — daily technical communication)
🌍 Location: Remote within EU (must hold EU Citizenship, PR, or a valid EU Work/Residence Permit)
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🎯 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.
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🛠 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.
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📩 To Apply:
Send your CV and expected hourly rate to Показать контакты
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