Artificial Intelligence Engineer
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We are looking for an experienced Data Scientist to join our team. As a key member of our engineering team, you will be responsible for developing innovative capabilities for open source platforms used for building, training, testing, and serving models for AI-enabled applications.
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The ideal candidate will have a strong background in machine learning and experience working with Python development. They should also be familiar with AI and Machine Learning platforms, tools, and frameworks, such as Tensorflow, PyTorch, LLaMA.cpp, and Kubeflow.
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This role requires a collaborative attitude and the ability to quickly learn and use new tools and technologies. The successful candidate will be able to work effectively in an agile development team and participate in upstream open source communities.
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Key Responsibilities:
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1. Develop innovative capabilities for open source platforms used for building, training, testing, and serving models for AI-enabled applications.
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2. Monitor and participate in upstream open source AI/ML communities, evaluating new AI/ML-related technologies in the space and considering potential integrations and collaborations upstream.
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3. Regularly read papers and keep up with AI/ML developments, particularly in the Generative AI space.
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4. Promote and foster Red Hat's open-source value proposition as it pertains to AI/ML engineering and product infusion and development.
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5. Contribute to the development of the open-source projects that comprise Red Hat's AI family of products.
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6. Take part in the shared responsibility of delivering and maintaining assigned products.
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7. Regularly communicate with project stakeholders including other teams of engineers, product managers, consultants, management, and senior leadership.
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8. Take on the role of Subject Matter Expert, as needed, per domain and project.
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9. Lead, coach and collaborate with junior engineers as they build AI/ML knowledge and skills.
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10. Coordinate and collaborate with external teams, including IBM Research on key strategic vision and implementation details.
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11. Share our experiments and learnings from an experienced position in the Data Science community through blogs, presentations, new ideas and existing work at various technical outlets and conferences.
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12. Promote upstream acceptance and community building.
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