When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Architect and manage Large Language Model (LLM) deployments across on-premises (NVIDIA/AMD) and cloud (cloud computing platform, Google Cloud platform (GCP) environments. Audit multi-agent orchestration, agent construction, and vector databases to map data flows and enforce privilege boundaries.
- Use Docker and Kubernetes to orchestrate scalable inference and training environments, optimizing Graphics Processing Unit (GPU) utilization and resource isolation.
- Protect model weights, secure data ingestion, and harden inference endpoints across the Machine Learning operations (MLOps) lifecycle. Investigate and mitigate AI-specific threats (e.g., prompt injection, jailbreaking, data poisoning). Map testing findings to MITRE ATLAS, OWASP for LLMs, and STRIDE models.
- Bridge local high-compute clusters and cloud AI services while maintaining a consistent security posture.
Qualifications
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 10 years of experience in AI/ML development, AI infrastructure engineering, or software development.
- 5 years of experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).
- 5 years of experience with Python and with libraries like PyTorch, TensorFlow, or Hugging Face Transformers.
- Ability to travel up to 25% of the time as needed.
Preferred qualifications:
- 5 years of experience in AI/ML research or software development.
- Experience with LLM deployment frameworks such as vLLM, NVIDIA Triton, or Ollama and agent development.
- Knowledge of open worldwide application security project (OWASP) for LLMs or similar security frameworks.
- Familiarity with cloud-native AI services (e.g., cloud computing platform, Google Vertex AI).
- Track record of deploying AI models on air-gapped or on-premises high-performance computing (HPC) systems.