Forward Deployed Engineer at Google (Google Cloud)

Google Cloud hires Forward Deployed Engineers in Bengaluru to take AI agents from prototype to production inside large customers. It is also a demanding one: the listings ask for real production experience and the interview tests customer skills as seriously as code.

What a Google FDE does

Google’s Bengaluru FDE postings sit in its applied and generative AI teams. Across the Cloud Applied AI and Generative AI (GECX) roles, the FDE:

• Co-builds with strategic customers. Designs, develops, debugs and deploys AI agents and solutions with the customer’s engineers, writing bespoke code and custom tooling.
• Connects Google’s AI to messy reality. Integrates conversational and agentic products with customer APIs, legacy data and security boundaries.
• Builds evaluation and observability. Pipelines to improve agent reasoning, tool selection and latency.
• Turns field work into product. Converts repeated customer problems into reusable modules, documentation or feature requests for engineering.
• Advises on production readiness. Agent improvement, performance tuning and CI/CD pipelines.

Google’s FDE II posting for another region describes the same pattern, adding MCP servers, tool calling and OAuth for secure agentic workflows. Expect these to come up for India roles too.

Experience asked

There is no fresher FDE route in these listings. Engineers early in their career usually reach Google FDE roles after a few years of backend, data or ML engineering with some customer exposure.

Pay in India

Google does not publish pay on its India FDE postings. Scaler estimates ₹35–55 LPA total compensation at L4 and ₹55–90+ LPA at L5, mapping FDE roles to Google’s software engineering bands on Glassdoor and Levels.fyi, and says FDE-specific India data is limited. Those figures fit the GCC and global platform bands in our FDE Salary Estimator.

How to prepare

1. Practise integration coding, not puzzles only. Write clean code that ingests messy data: parse logs, normalise events, load a legacy table into a warehouse. Candidates report these tasks.
2. Design into an existing system. Rehearse “how would you add Vertex AI to this bank’s on-prem data without moving regulated records?” Draw the current system first, then the change.
3. Build and evaluate one agent on Google Cloud. Vertex AI or Dialogflow plus RAG over real documents, with an eval set for accuracy and latency. It gives you concrete stories for every round.
4. Prepare customer stories with numbers. A stakeholder who disagreed, a deadline that slipped, a launch you owned. The customer round is the one people under-prepare for.
5. Check level fit. With under 2 years, aim first for Google Cloud customer engineering or adjacent roles; with 5+ years, target the GenAI FDE roles directly.

See also our comparison of roles similar to the FDE, and use the FDE Alignment Checker to see which skill areas you need to strengthen.

Google (Google Cloud) FDE interview: what is publicly known

1. Recruiter screen: Checks role fit and customer-facing motivation early, according to candidate reports summarised by Scaler. This is a reported pattern, not an official process, and it varies by team and level.
2. Coding: Practical data and integration problems, such as normalising event streams or building a pipeline from a legacy database into BigQuery. Clean, maintainable code matters because customers inherit it.
3. System design and integration: Fitting Google products into an existing customer system rather than greenfield design, for example connecting on-prem data to Vertex AI or migrating an ETL pipeline from another cloud.
4. Customer-facing and behavioural: Explaining technical work to non-technical people, handling ambiguity and difficult customer conversations, and owning outcomes. Scaler notes most candidates under-prepare for this round.