Senior Machine Learning Engineer
1 month ago
At Oracle Cloud Infrastructure (OCI), we build the future of the cloud for Enterprises as a diverse team of fellow creators and inventors. We act with the speed and attitude of a start-up, with the scale and customer-focus of the leading enterprise software company in the world.
Oracle Generative AI Service is an exciting team in Oracle Cloud Infrastructure. We are delivering innovative services at the intersection of artificial intelligence and cloud infrastructure. In Generative AI Service team, you will build and operate massive-scale cloud services leveraging state of art machine learning technologies. We are committed to providing the best in cloud products to meet the needs of our customers who are tackling some of the world’s most challenging problems.
You will be part of a team of smart, hands-on scientists and engineers with the expertise and passion to solve difficult problems in distributed highly available services and virtualized infrastructure. At every level, our engineers have a significant technical and business impact by designing and building innovative new systems to power our customer’s business critical applications.
As a Senior Machine Learning Engineer in Generative AI Service team, you will play a key role in shaping the future of Generative AI at Oracle, with an emphasis on Large Language Models (LLMs) and Multi-modality Models. Your contributions will be pivotal in delivering our new Generative AI-powered Agent services for large scale enterprise customers involving Retrieval Augmented Generation, Code Generation, AI Agent and other use cases.
Responsibilities:
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.Qualifications and Experience:
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. Good coding experience in Python and ML frameworks Proven experience in designing data collection/annotation solutions and systematic evaluation necessary for developing and maintaining production systems. Commitment to staying up-to-date with the field and applying academic advances to solve complex business problems, and bringing them into production. Strong knowledge of software systems in Cloud and software engineering conceptsPreferred Qualifications:
Knowledge of LLM and experience delivering Generative AI And Agent models are a significant plus. Strong publication record, including as a lead author or reviewer, in top-tier journals or conferences. Familiarity and experience with the latest advancements in computer vision and multimodal modeling is a plus. 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.-
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