Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
- Experience integrating generative AI tools or Large Language Model (LLM) interfaces into workflows.
- Experience with Kubernetes, C , and Go programming.
- Experience working with Apps Framework, Distributed Systems, Spanner, Google Cloud Platform, One Platform, and Compliance.
Preferred qualifications:- Master's degree or PhD in Engineering, Computer Science, or a related technical field.
- 8 years of experience with data structures and algorithms.
- 3 years of experience with distributed system design of infrastructure software.
- Experience building or operating large-scale services on Google Cloud Platform (GCP), specifically leveraging technologies like Spanner, IAM, OnePlatform, GKE, or Compute Engine, and familiar with Compliance requirements such as CMEK and Wipeout.
- Track record of solving ambiguous infrastructure problems, such as system performance bottlenecks or multi-region availability.
- Motivated Tech Lead (TL) who is passionate about building reliable, scalable foundation infrastructures that powers many AI products.
About the jobBuild infrastructure that powers many Vertex AI products and solutions, including Gen AI, Agent, Document AI, Vision AI, etc. Build foundational infrastructure to make Vertex AI more enterprise ready by providing reliable and scalable infrastructure, and advanced security and compliance features (e.g. access control, auth, security horizontals).
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Lead, design and implement business critical Infra for Vertex AI products. Solve complex backend system problems to address bottlenecks for system scalability and reliability, adapting the system to rapid business growth.
- Drive implementation to make Cloud AI products meet security and compliance requirements in order to unblock customers in certain industries (e.g., financial industry, healthcare).
- Work closely with other AI initiatives within Google, including Gemini serving, Partner MaaS, Agents.
- Build Vertex AI into the full lifecycle of customer's machine learning workflows by improving interoperability within multiple Vertex AI products and with other GCP/non-GCP services (e.g., Kaggle, HuggingFace, TFHub).
- Learn and apply emerging AI infrastructure patterns (e.g., model lifecycle automation) to build a foundation for Vertex AI.
