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Описание вакансии
TL;DR
ML Engineer (Generative AI) (PyTorch/TensorFlow): Developing, evaluating, and deploying generative models for image, video, text, and multimodal applications with an accent on scalable training, evaluation, inference, and MLOps pipelines. Focus on optimizing latency, throughput, memory usage, output quality, infrastructure costs, and production model deployment on GPUs or cloud platforms.
ML Engineer
METEOR
1,000,000 to 1,500,000 KZT NET per month
Remote
AI platform for communication.
Responsibilities:
• Develop, evaluate, and deploy generative models for image, video, text, and multimodal applications.
• Build scalable data, training, evaluation, real-time inference, and MLOps pipelines; optimize latency, throughput, memory usage, quality, and infrastructure costs.
• Integrate models into production systems, automate deployment and monitoring, and research and assess generative AI methods for production use.
Requirements:
• 3+ years of machine learning and software development experience, including generative AI models.
• Deep understanding of diffusion and modern image/video-generation architectures.
• Experience with LLM prompting, fine-tuning, evaluation, structured outputs, and tool use.
• Proficiency in PyTorch or TensorFlow and production model deployment on GPUs or cloud platforms.
METEOR
1,000,000 to 1,500,000 KZT NET per month
Remote
AI platform for communication.
Responsibilities:
• Develop, evaluate, and deploy generative models for image, video, text, and multimodal applications.
• Build scalable data, training, evaluation, real-time inference, and MLOps pipelines; optimize latency, throughput, memory usage, quality, and infrastructure costs.
• Integrate models into production systems, automate deployment and monitoring, and research and assess generative AI methods for production use.
Requirements:
• 3+ years of machine learning and software development experience, including generative AI models.
• Deep understanding of diffusion and modern image/video-generation architectures.
• Experience with LLM prompting, fine-tuning, evaluation, structured outputs, and tool use.
• Proficiency in PyTorch or TensorFlow and production model deployment on GPUs or cloud platforms.
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