Staff Applied AI Agent Developer

Google Google · Big Tech · Waterloo, ON +4

Staff Applied AI Agent Developer role focused on applying Generative AI (Gemini Enterprise) to solve real-world challenges for strategic customers. Responsibilities include designing, co-developing, debugging, and deploying custom conversational AI agents, writing bespoke code, developing custom tooling, and providing technical guidance to customers. The role also involves creating reusable tools, building documentation, and influencing product strategy based on field insights.

What you'd actually do

  1. Partner directly with select, strategic customers to understand their business challenges. Design, co-develop, debug, and deploy custom conversational AI agents and solutions to accelerate their time to value.
  2. 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.
  3. Systematize your learnings from customer engagements by creating reusable tools, building documentation and accelerators, and establishing best practices that can be used across the organization.
  4. Serve as a critical feedback loop to our core Product and Development teams. Synthesize insights from the field to influence product strategy, identify feature gaps.
  5. Act as a subject matter expert, providing technical guidance and best practices to customers on agent improvement, performance tuning, CI/CD pipelines, and production readiness.

Skills

Required

  • software development
  • software design and architecture
  • speech/audio
  • reinforcement learning
  • ML infrastructure
  • ML specialization
  • core GenAI concepts
  • Large Language Model (LLM)
  • multi-modal
  • large vision models
  • text generation
  • image generation
  • video generation
  • audio generation

Nice to have

  • prompt development
  • model evaluation
  • creative application of Artificial Intelligence (AI)
  • Vertex AI
  • BigQuery
  • Cloud Storage
  • Dialogflow
  • flexibility and resilience in dynamic environments
  • ownership
  • respond to urgent business or customer issues

What the JD emphasized

  • custom conversational AI agents
  • custom tooling
  • critical customer issues
  • agent improvement
  • performance tuning
  • production readiness

Other signals

  • customer-facing AI solutions
  • building AI agents
  • deploying AI agents
  • optimizing AI agents
  • working with strategic customers