GenAI Architect - Vertex AI for Insurance

Capgemini; Hartford (CT); Hybrid; USD 130,000 - 140,000; Full time

Job summary

Capgemini is seeking an AI Architect to own end-to-end GenAI and Vertex AI architectures for insurance clients. You will shape pre-sales directions, oversee delivery teams, and ensure secure, governed, scalable, and cost-efficient solutions.

Responsibilities include defining reference architectures for RAG, selecting models, designing data foundations with BigQuery and Vertex AI tools, and leading LLMOps and Responsible AI reviews across programs.

Qualifications

• 12+ years in IT, 5+ in cloud/data/AI architecture, 2+ designing production GenAI and Vertex AI.
• Strong expertise in GCP tech and Vertex AI governance.
• Ability to review engineers' code and prototype solutions.

Responsibilities

• Own end-to-end GenAI and agentic architecture on Google Cloud for insurance clients.
• Set technical direction for delivery teams and ensure secure, governed, scalable solutions.
• Define reference architectures for RAG and multi-agent systems on Vertex AI.
• Design data foundations: BigQuery, Cloud Storage, AlloyDB, Vector Search / Vertex AI Search, Document AI, Dataflow and Pub/Sub.
• Set integration patterns with policy admin, claims and underwriting systems through Apigee and event-driven APIs.
• Own security and compliance: IAM, CMEK, VPC Service Controls, audit logging, data residency.
• Set up LLMOps: evaluation, prompting, model versioning, monitoring, drift tracking, human-in-the-loop reviews.
• Lead Responsible AI reviews: explainability, bias testing, model risk docs for regulators.

Skills

Vertex AI

Gemini Enterprise Agent Platform

Python

LLM evaluation

Terraform

CI/CD for ML/GenAI

RAG

Security & compliance

Stakeholder management

Tools

Gemini Enterprise Agent Platform

Gemini API

Agent Builder

Cloud Run

GKE

BigQuery

Apigee

IAM

Networking

Job description

Capgemini is seeking an AI Architect to own end-to-end GenAI and Vertex AI architectures for insurance clients. You will shape pre-sales directions, oversee delivery teams, and ensure secure, governed, scalable, and cost-efficient solutions.

Responsibilities include defining reference architectures for RAG, selecting models, designing data foundations with BigQuery and Vertex AI tools, and leading LLMOps and Responsible AI reviews across programs.