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О рекламодателе
ОБЩЕСТВО С ОГРАНИЧЕННОЙ ОТВЕТСТВЕННОСТЬЮ "ЦЕНТР НАЦИОНАЛЬНЫХ ИНТЕЛЛЕКТУАЛЬНЫХ СИСТЕМ" ИНН: 9704271170
описание
The role focuses on designing, deploying, and maintaining AI/ML solutions in production environments, combining Machine Learning Engineering, MLOps, and GPU infrastructure administration and optimization to support model training and inference workloads.
задачи
Design and develop AI and Machine Learning solutions
Build and maintain MLOps pipelines for CI/CD of ML models
Automate model training, deployment, and monitoring processes
Manage the end-to-end Machine Learning Lifecycle
Collaborate closely with Data Scientists, Software Engineers, and business stakeholders
Optimize compute resource utilization, particularly in GPU-based environments
Monitor model performance, quality, and data drift
Contribute to the design of AI architectures on cloud platforms
Create technical documentation and establish engineering best practices
требования
At least 3 years of experience in AI/ML projects
Strong Python skills
Practical experience with PyTorch or TensorFlow, Scikit-learn, and Hugging Face
Experience with MLOps technologies, including MLflow, Kubeflow, and Azure Machine Learning, Vertex AI, or SageMaker
Experience with CI/CD tools such as GitHub Actions, Azure DevOps, or GitLab CI
Experience with Docker and Kubernetes
Practical experience working with distributed AI/ML environments and compute clusters
Good understanding of cloud platforms (Azure, AWS, or GCP) or on-premise systems
Understanding of workload orchestration, resource allocation, and scaling for Machine Learning workloads
Experience with Git and modern DevOps practices
English proficiency at B2 level or higher
Strong ownership and accountability for delivered solutions
Ability to translate business requirements into technical implementations
Analytical, problem-solving mindset
Strong organizational and collaboration skills in agile environments
Будет плюсом: experience with LLMs and Generative AI solutions, knowledge of RAG, Vector Databases, and Agentic AI, experience with cloud-based GPU platforms (Azure AI, AWS EC2 P4/P5, GCP A3), experience managing or operating GPU-enabled Kubernetes clusters, experience with NVIDIA GPU Operator, KServe, Ray, Kubeflow, or similar technologies, understanding of cluster monitoring and performance optimization in AI environments, familiarity with Spark, Databricks, or Ray
