Capgemini is seeking a GenAI Lead / GCP AI Engineer Lead to build and productionize Google Cloud based GenAI solutions for insurance clients.

Build GenAI applications using Gemini models on the Gemini Enterprise Agent Platform (Vertex AI).
Design and tune prompts for insurance document use cases using structured outputs and function calling (submissions, ACORD forms, COIs, loss runs, policies).
Develop RAG pipelines including chunking, embeddings, Vector Search / Vertex AI Search, and grounding with citations.
Create and deploy agents using Agent Development Kit (ADK) on Agent Engine, integrating tools and APIs.
Extract and classify documents with Document AI using Gemini multimodal input.
Ship services on Cloud Run or GKE, expose through Apigee, and implement CI/CD in Cloud Build.
Move and manage data using BigQuery, Cloud Storage, Pub/Sub, and Dataflow.
Define evaluation sets and run quality, latency, and cost checks using Gen AI evaluation service and golden datasets.
Apply guardrails including PII masking via Sensitive Data Protection (DLP), safety filters, and IAM least privilege.
Collaborate in Agile sprints with QA, UI, and data teams; write clean, tested, documented code.

Strong Python experience with FastAPI or Flask, including async development and pytest testing.
Hands-on experience with the Gemini API and/or Vertex AI SDK, or the Google Gen AI SDK.
RAG architecture experience plus at least one vector store such as Vector Search, AlloyDB pgvector, or BigQuery vector search.
LLM framework experience using ADK, LangChain, or LangGraph.
Core GCP capabilities including Cloud Run, Cloud Functions, Cloud Storage, BigQuery, IAM, and Secret Manager.
REST API design, Git, Docker, and CI/CD.
Good written and verbal communication with onsite and client teams.

Python, FastAPI, Flask, pytest
Gemini API, Vertex AI SDK, Google Gen AI SDK
Gemini Enterprise Agent Platform (Vertex AI), Agent Development Kit (ADK), Agent Engine
Document AI, Gemini multimodal input
Cloud Run, GKE, Apigee, Cloud Build
BigQuery, Cloud Storage, Pub/Sub, Dataflow
Sensitive Data Protection (DLP), IAM, Cloud Functions, Secret Manager
REST API, Git, Docker
Vector Search, Vertex AI Search, AlloyDB pgvector, BigQuery vector search
LangChain, LangGraph
Gen AI evaluation service, CI/CD, Terraform, Looker, Looker Studio
Model Context Protocol (MCP), Agent2Agent (A2A) protocol

Paid time off based on employee grade (A-F) per policy: Vacation: 12-25 days depending on grade, Company paid holidays, Personal Days, Sick Leave
Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
Life and disability insurance
Employee assistance programs
Other benefits as provided by local policy and eligibility

Insurance domain experience (P&C, E&S, specialty, workers' comp) or financial services
Document AI custom extractors; OCR on scanned or handwritten forms
Agent2Agent (A2A) protocol or Model Context Protocol (MCP) tool integration
Model tuning (supervised fine-tuning on Gemini) and model monitoring
Terraform; Looker or Looker Studio dashboards

GenAI Lead / GCP AI Engineer Lead

Job Description