Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 10 years of experience with cloud native architecture in a customer-facing or support role.
• Experience engaging with, or presenting to, technical stakeholders or executive leaders.
• Experience with machine learning (ML) model development and deployment.
• Active US Government Top Secret/Sensitive Compartmentalized Information (TS/SCI) security clearance with polygraph.
• 
  Ability to travel up to 20% of the time.

Preferred qualifications:

• Experience with prevailing ML development frameworks (e.g., Keras, PyTorch, Tensorflow, JAX).
• Experience with both GPU and TPU based infrastructure.
• Familiarity with prevailing AI related tooling (Slurm, vLLM, Ray, Vertex, K8s, etc.).
• Familiarity across the AI software development life cycle (data processing, model building, training, evaluation, deployment).
• Ability to deliver results and work cross-functionally to position and orchestrate a solution consisting of multiple products.

About the job

You will focus on identifying, pursuing, and winning new business workloads and driving pen testing within existing ones. You will have a breadth of technical expertise, spanning infrastructure modernization, application modernization, data analytics and more. You will blend sales expertise, market knowledge and direct technical engagement to show the value of the Google Cloud portfolio.

Responsibilities

• Accelerate customer time-to-value on the largest AI Infrastructure and High Performance Computing (HPC) workloads in Google Public Sector.
• Build a trusted advisory relationship with customer architects, engineering leadership, and research teams. Identify customer priorities, technical objections and design strategies focused on Google AI Infrastructure and HPC ecosystem to deliver business value and resolve blockers.
• Provide domain expertise around hardware accelerators (GPU/TPU), prevailing ML Frameworks (PyTorch, Keras, JAX), and model building techniques.
• Make recommendations on Graphics Processing Unit/Tensor Processing Unit (GPU/TPU) hardware, framework selection, benchmarks, and model building required to successfully implement a complete solution.
• Manage the holistic research engineering relationship with customers by collaborating with specialists, product management, technical teams, and more.