Cloud Platform Engineer

About Global Foundries:
Global Foundries is a leading full-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the world’s most inspired technology companies. With a global manufacturing footprint spanning three continents, Global Foundries makes possible the technologies and systems that transform industries and give customers the power to shape their markets.

We are seeking a highly skilled Cloud Platform Engineer to help us build the next‑generation Platform around Silicon design. Our goal is to build solutions that provide our customers and partners with an experience that allows them to obtain the highest insights from silicon design knowledge. In this role, you will be involved in various aspects of infrastructure, including natural language processing, vector databases, knowledge graphs, machine learning pipelines, model training and inference, semantic search optimization, retrieval‑augmented generation and multi‑modal AI deployments.

You will also work directly with customers at all stages to gather requirements and improve deliverables based on feedback. This is an exciting opportunity to work in a dynamic environment, that involves interacting with our engineering teams — from design and verification to performance. Security is a crucial component as we build models from the ground up! If you are an innovative problem solver with a deep passion for infrastructure and machine learning technologies, and love building things from the ground up, we want to hear from you.

• Natural language processing and semantic understanding for technical documentation
• Vector database development and management for IP knowledge retrieval
• Machine learning pipeline development for PyTorch, Tensor Flow and other relevant frameworks on GPU and cloud environments
• Use AI monitoring tools to analyze and optimize models, through MLOps platforms and performance tuning
• Code‑review complex AI model contributions in any of these areas
• Interact with architecture and silicon design teams to implement our next AI platform – provide inputs and drive architectural decisions based on user query needs
• Help maintain the documentation and reference implementations that our customers rely on
• Work with customers to enable rapid integration of their IP queries for evaluation and development

• Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs.

• Minimum 10 Years in Software Engineering or related field
• Strong experience with Python and machine learning frameworks
• Knowledge of AI platform concepts (e.g. transformers, embeddings, fine‑tuning, model deployment)
• Experience developing AI inference pipelines or training workflows in cloud environments and containerized platforms
• Proven experience with large language models on different cloud architectures (multi‑modal experience would be great!)
• Experience with Git, Docker, ML pipelines and model automation scripting
• Experience with vector databases, RAG systems, knowledge graphs, prompt engineering or AI platform optimization
• Experience working with platform architecture and engineering teams

• Master’s in Computer Engineering or related field OR 15+ years in Software Engineering or related field.
• Familiarity with AI‑native technologies. Knowledge of different AI Platforms (e.g. OpenAI, Anthropic, Hugging Face)
• Experience working with MLOps and model automation tools
• Experience with retrieval‑augmented generation (e.g. Lang Chain, Llama Index, or custom RAG architectures)
• Strong communication, collaboration, and listening skills
• Strong planning & organizational skills

Expected Salary Range $ - $. The exact Salary will be determined based on qualifications, experience and location.

If you need a reasonable accommodation for any part of the employment process, please contact us by email at  and let us know the nature of your request and your contact information. Requests for accommodation will be considered on a case‑by‑case…