AI Engineer
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The role
Lead design, deployment, and scaling of generative AI systems powered by LLMs and diffusion models.
Deploy and monitor large-scale models for text, code, or multimodal reasoning using Azure resources.
Own the end-to-end ML lifecycle: data preprocessing, evaluation, deployment, and monitoring.
Develop prompts, tune models, and build internal tools or APIs to integrate generative capabilities into production.
Collaborate with product and engineering teams to translate business goals into AI solutions and stay current with GenAI research.
Requirements include deep ML/AI knowledge, transformer architectures, prompt engineering, Python, cloud tools, Docker/Kubernetes, and strong communication.
Deploy and monitor large-scale models for text, code, or multimodal reasoning using Azure resources.
Own the end-to-end ML lifecycle: data preprocessing, evaluation, deployment, and monitoring.
Develop prompts, tune models, and build internal tools or APIs to integrate generative capabilities into production.
Collaborate with product and engineering teams to translate business goals into AI solutions and stay current with GenAI research.
Requirements include deep ML/AI knowledge, transformer architectures, prompt engineering, Python, cloud tools, Docker/Kubernetes, and strong communication.
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