1 day ago
Responsibilities
- Apply knowledge of virtualization, containerization, operating systems, and system administration.
- Work with GCP, AWS, and Azure cloud platforms and their AI/ML services.
- Apply generative AI and LLM expertise using Google Cloud products and services.
- Develop and deploy machine learning models.
- Use deep-learning frameworks, AI accelerators, model architectures, and machine learning APIs.
- Support cloud platform architecture, deployment, and management of cloud-based systems.
- Develop, categorize, map, and maintain technical skill taxonomies.
- Integrate new offerings into existing practices while minimizing disruption and optimizing results.
- Apply knowledge of IaaS, PaaS, and SaaS cloud service models.
Requirements
- Deep understanding of compute fundamentals, including virtualization, containerization, operating systems, and system administration.
- Experience with GCP, AWS, and Azure, including their AI/ML services.
- Experience with LLMs, generative AI, and Google Cloud products and services such as Vertex AI, Dialogflow, and Gemini.
- Experience developing and deploying machine learning models.
- Experience with PyTorch, TensorFlow, JAX, Ray, TPUs, GPUs, encoders, decoders, transformers, and machine learning APIs.
- Familiarity with cloud platform engineering architecture, deployment, and management.
- Knowledge of skill taxonomy development and implementation, including categorization, mapping, and maintenance.
- Familiarity with IaaS, PaaS, and SaaS models.
Tech Stack
Categories
Solutions Engineering
About IBM
IBM is a public technology company that sells enterprise software, hybrid-cloud infrastructure, AI platforms, and mainframe systems, alongside consulting and managed services for global businesses and governments. Founded in 1911 and headquartered in Armonk, New York, it trades on the NYSE as IBM. Its portfolio includes Red Hat OpenShift and watsonx, and it operates IBM Research to support advances in computing and industry applications.