Cloud Platform Engineer

Job Title

Cloud Platform Engineer

Role Summary

Build and operate a cloud-based AI platform to support silicon design workflows, focusing on NLP, vector search, knowledge graphs, ML pipelines, and model deployment. Work with engineering and customer teams to gather requirements and deliver production-ready platform components.

Primary responsibilities cover infrastructure, model training/inference, retrieval-augmented systems, and MLOps with an emphasis on security and performance.

Experience Level

Senior — minimum 10 years in software engineering or a related field (senior/architect-level experience expected).

Responsibilities

Core day-to-day responsibilities include:

• Develop NLP and semantic understanding for technical documentation and IP search.
• Design, implement and manage vector databases and retrieval systems for knowledge retrieval.
• Build and maintain ML training and inference pipelines (PyTorch, TensorFlow) for GPU and cloud environments.
• Implement MLOps, model monitoring, performance tuning, and automation for model lifecycle management.
• Perform code reviews for complex AI model and infrastructure contributions.
• Collaborate with architecture and silicon design teams to define platform requirements and drive architectural decisions.
• Maintain documentation and reference implementations for customers and internal teams.
• Work directly with customers to enable rapid integration and evaluation of their IP queries.
• Follow Environmental, Health, Safety & Security requirements in all activities.

Requirements

Must-have and preferred skills; core requirements are listed first.

• Must-have: Minimum 10 years in software engineering or related field; strong Python skills.
• Must-have: Experience with machine learning frameworks (PyTorch, TensorFlow) and ML pipelines in cloud and containerized environments.
• Must-have: Experience deploying and operating large language models and multi-modal models across cloud architectures.
• Must-have: Experience with Git, Docker, containers, and automation scripting for ML workflows.
• Must-have: Experience with vector databases, retrieval-augmented generation (RAG) systems, knowledge graphs, or prompt engineering.
• Must-have: Experience designing and integrating AI inference/training pipelines and applying MLOps practices.
• Nice-to-have: Familiarity with AI platforms and tools (OpenAI, Anthropic, Hugging Face), LangChain, LlamaIndex, or custom RAG architectures.
• Nice-to-have: Experience with AI-native technologies, model automation tooling, and multi-modal AI deployments.
• Other: Strong communication, collaboration, planning, and organizational skills.

Education Requirements

Preferred: Master’s degree in Computer Engineering or a related field. The posting accepts equivalent practical experience (the posting cites 15+ years as an alternative to a master’s). No mandatory bachelor/undergraduate degree is explicitly required in the source text.

About the Company

Company: GlobalFoundries

Headquarters: Saratoga Springs, New York, USA

GlobalFoundries is a leading contract manufacturer for the global semiconductor industry, with facilities in multiple countries, including the USA. The company develops a broad portfolio of semiconductor technologies and employs around 13,000 people worldwide. GlobalFoundries focuses on enhancing competitiveness in specialized application solutions and fostering innovation in mobile communications, consumer electronics, and automotive applications.