GenAI / GCP AI Engineer

Capgemini; Hartford (CT); Remote; USD 140,000 - 230,000; Full time

Job summary

Capgemini is seeking a GenAI Platform Engineer to build, test and deploy production GenAI solutions on Google Cloud for insurance clients, including document summarization, intelligent intake, underwriting and claims assistants, and agentic workflows. You will turn architecture into working code and own features from prototype to production.

In this role you will design prompts, implement RAG pipelines, integrate Gemini and Vertex AI SDKs, build agents with the ADK, and ensure guardrails, CI/CD

Qualifications

• Strong Python and API service development (FastAPI or Flask).
• Hands-on Gemini API / Vertex AI SDK or Google Gen AI SDK experience.
• Design and tune prompts, structured outputs and function calling for GenAI workflows.
• Experience with RAG design and at least one vector store (Vector Search / BigQuery vector search).
• Familiarity with Agile development processes and working with QA/UI/data teams.

Responsibilities

• Build GenAI applications with Gemini models on Gemini Enterprise Agent Platform (Vertex AI).
• Design and tune prompts, structured outputs and function calling for insurance documents (submissions, ACORD forms, COIs, loss runs, policies).
• Build RAG pipelines: chunking, embeddings, Vector Search / Vertex AI Search, grounding and citations.
• Build agents with the ADK and deploy them on Agent Engine; integrate tools and APIs.
• Extract and classify documents with Document AI and Gemini multimodal input.
• Ship services on Cloud Run or GKE, exposed through Apigee, with CI/CD in Cloud Build.
• Move and store data with BigQuery, Cloud Storage, Pub/Sub and Dataflow.
• Write evaluation sets and run quality, latency and cost checks.
• Apply guardrails: PII masking with DLP, safety filters, IAM least privilege.
• Work in Agile sprints with QA, UI and data teams; write clean, tested, documented code.

Skills

Python

Async programming

Prompts design

Pytest

LLM concepts

Education

Google Cloud Professional ML Engineer (cert)

Google Cloud Generative AI Leader (cert)

Tools

Gemini API

Vertex AI SDK

Google Gen AI SDK

ADK (Agent Development Kit)

LangChain

LangGraph

Job description

Build, test and deploy production GenAI solutions on Google Cloud for insurance clients: document summarization, intelligent intake, underwriting and claims assistants, and agentic workflows. You turn architecture into working code and own features from prototype to production.

Key responsibilities

Build GenAI applications with Gemini models on Gemini Enterprise Agent Platform (Vertex AI).

Design and tune prompts, structured outputs and function calling for insurance documents (submissions, ACORD forms, COIs, loss runs, policies).

Build RAG pipelines: chunking, embeddings, Vector Search / Vertex AI Search, grounding and citations.

Build agents with the Agent Development Kit (ADK) and deploy them on Agent Engine; integrate tools and APIs.

Extract and classify documents with Document AI and Gemini multimodal input.

Ship services on Cloud Run or GKE, exposed through Apigee, with CI/CD in Cloud Build.

Move and store data with BigQuery, Cloud Storage, Pub/Sub and Dataflow.

Write evaluation sets and run quality, latency and cost checks (Gen AI evaluation service, golden datasets).

Apply guardrails: PII masking with Sensitive Data Protection (DLP), safety filters, IAM least privilege.

Work in Agile sprints with QA, UI and data teams; write clean, tested, documented code.

Required skills

Strong Python (FastAPI or Flask, async, pytest).

Hands-on Gemini API / Vertex AI SDK or Google Gen AI SDK.

RAG design and at least one vector store (Vector Search, AlloyDB pgvector, BigQuery vector search).

LLM frameworks: ADK, LangChain or LangGraph.

Good written and verbal communication with onsite and client teams.

Nice to have

Insurance domain (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.

Certifications (preferred)

Google Cloud Professional Machine Learning Engineer

Google Cloud Generative AI Leader (good to have)"