Software Engineer, Applied Ai, Agent Building, Google Cloud Platform

Google Google · Big Tech · London, United Kingdom

Software Engineer role focused on applying Google's Generative AI to build, deploy, and optimize AI agents for enterprise customers on the Google Cloud Platform. This role involves direct customer engagement, co-development of conversational AI agents, and contributing to core product improvements.

What you'd actually do

  1. Partner directly with customers to understand their business issues. Design, co-develop, debug, and deploy custom conversational AI agents and solutions to accelerate their time to value.
  2. Systematize learnings from customer engagements by creating reusable tools, building documentation and accelerators, and establishing best practices across the organization.
  3. Act as a high-level problem solver, empowered to write bespoke code, develop custom tooling, and even contribute directly to the core product codebase to resolve critical customer issues.
  4. Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.

Skills

Required

  • software design and architecture
  • Speech/audio processing
  • reinforcement learning
  • ML infrastructure
  • ML design
  • model deployment
  • model evaluation
  • data processing
  • debugging
  • fine tuning

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • data structures and algorithms
  • complex, matrixed organization involving cross-functional, or cross-business projects.

What the JD emphasized

  • 5 years of experience testing, and launching software products
  • 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
  • 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).

Other signals

  • customer-facing AI solutions
  • building AI agents
  • optimizing ML infrastructure
  • working with Generative AI