Google Forward Deployed Engineer - Gps

The Google Forward Deployed Engineer (FDE) role at Deloitte focuses on building and deploying GenAI-enabled solutions and agentic platforms for enterprise clients. The role involves working closely with clients to identify use cases, prototype, and deliver working AI solutions, emphasizing scalable engineering patterns, tool-use approaches, and human-in-the-loop controls. The engineer will be responsible for delivering production-quality code with strong testing, CI/CD, and documentation practices, and will apply architecture decisions balancing quality, safety, latency, cost, and model risk.

What you'd actually do

  1. Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
  2. Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
  3. Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
  4. Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
  5. Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.

Skills

Required

  • 4+ years of experience in software engineering, data engineering, data science, or analytics engineering.
  • 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
  • 1+ years of experience with Google including hands on experience with one of the following key platform technologies; Gemini API, Vertex AI Agent Builder, Grounding, Google Workspace integration
  • 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
  • 1+ years of experience building reliable, maintainable, and well-documented code

Nice to have

  • Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
  • Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments
  • Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
  • Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
  • Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures
  • Experience operating within hybrid onshore/offshore teams
  • Familiarity with security, privacy, and compliance considerations

What the JD emphasized

  • GenAI-enabled solutions
  • agentic platforms
  • human-in-the-loop controls
  • production-quality code
  • Google including hands on experience with one of the following key platform technologies; Gemini API, Vertex AI Agent Builder, Grounding, Google Workspace integration

Other signals

  • GenAI-enabled solutions
  • agentic platforms
  • human-in-the-loop controls
  • production-quality code