Develop new OCI services and features leveraging recent advances in generative AI, machine learning and deep learning; Evaluate and benchmark AI models including LLMs, perform inference testing, optimise models and monitor model performance in production; Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services; Contribute to the architecture of generative AI, including data, model, training, and evaluation, employing best practices; Develop production code and advocate for the best coding and engineering practices; Participate in project planning, review, and retrospective sessions
Requirements:
Demonstrated experience in designing and implementing scalable AI models for production; Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers; Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts; Hands-on experience with emerging LLM frameworks and plugins, such as LangChain, LlamaIndex, VectorStores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc
Text:
Senior Machine Learning Engineer Develop new OCI services and features leveraging recent advances in generative AI, machine learning and deep learning; Evaluate and benchmark AI models including LLMs, perform inference testing, optimise models and monitor model performance in production; Collaborate with fellow technical leaders to ensure the successful and timely delivery of models and integration of services; Contribute to the architecture of generative AI, including data, model, training, and evaluation, employing best practices; Develop production code and advocate for the best coding and engineering practices; Participate in project planning, review, and retrospective sessions Demonstrated experience in designing and implementing scalable AI models for production; Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers; Practical experience with the latest technologies in LLM and generative AI, such as parameter-efficient fine-tuning, instruction fine-tuning, and advanced prompt engineering techniques like Tree-of-Thoughts; Hands-on experience with emerging LLM frameworks and plugins, such as LangChain, LlamaIndex, VectorStores and Retrievers, LLM Cache, LLMOps (MLFlow), LMQL, Guidance, etc
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