GCP Data Engineer (Vertex AI)

Capgemini • New York, NY

2d • $105,000 - $115,000 • Hybrid

About The Position

The GCP Data Engineer will build, test, and deploy production generative AI solutions on Google Cloud for insurance clients. The work includes document summarization, intelligent intake, underwriting and claims assistants, and agentic workflows. The engineer turns architecture into working code and owns features from prototype through production.

The role involves building Gemini-based GenAI applications, designing retrieval-augmented generation pipelines and agents, processing insurance documents, deploying services and integrating data across Google Cloud services. The engineer also evaluates quality, latency, and cost; applies privacy and security guardrails; and collaborates in Agile sprints with QA, UI, data, and client teams.

Location: Hartford, Connecticut, with three days per week in the office. The posted base compensation range is $105,000–$115,000. The role may also be eligible for variable incentives, bonuses, or commissions. Capgemini provides a benefits package to regular, full-time employees, with benefits and eligibility determined by local policy.

Requirements

• 4–8 years of experience in software, data, or ML engineering.
• 1–5 years of hands-on GenAI experience, including Vertex AI and Google Cloud Platform services.
• Strong Python skills and experience with FastAPI or Flask, asynchronous programming, and pytest.
• Hands-on experience with the Gemini API, Vertex AI SDK, or Google Gen AI SDK.
• RAG design experience and hands-on experience with at least one vector store, such as Vector Search, AlloyDB pgvector, or BigQuery vector search.
• Experience with an LLM framework such as ADK, LangChain, or LangGraph.
• Experience with Google Cloud core services, including Cloud Run, Cloud Functions, Cloud Storage, BigQuery, IAM, and Secret Manager.
• REST API design, Git, Docker, and CI/CD experience.
• Good written and verbal communication skills for working with onsite and client teams.

Nice To Haves

• Insurance domain experience, including property and casualty, specialty, workers' compensation, or financial services.
• Experience with Document AI custom extractors and OCR for scanned or handwritten forms.
• Experience with Agent2Agent (A2A) protocol or Model Context Protocol (MCP) tool integration.
• Experience with supervised fine-tuning on Gemini and model monitoring.
• Experience with Terraform and Looker or Looker Studio dashboards.
• Google Cloud Professional Machine Learning Engineer certification.
• Google Cloud Associate Cloud Engineer or Professional Cloud Developer certification.
• Google Cloud Generative AI Leader certification.

Responsibilities

• Build, test, and deploy production GenAI applications using Gemini models on Gemini Enterprise Agent Platform and Vertex AI.
• Design and tune prompts, structured outputs, and function calling for insurance documents, including submissions, ACORD forms, certificates of insurance, loss runs, and policies.
• Build RAG pipelines using chunking, embeddings, Vector Search, Vertex AI Search, grounding, and citations.
• Build agents with the Agent Development Kit (ADK), deploy them on Agent Engine, and integrate tools and APIs.
• Extract and classify documents using Document AI and Gemini multimodal input.
• Deploy services on Cloud Run or GKE, expose them through Apigee, and implement CI/CD using Cloud Build.
• Move and store data with BigQuery, Cloud Storage, Pub/Sub, and Dataflow.
• Create evaluation sets and conduct quality, latency, and cost checks using the Gen AI evaluation service and golden datasets.
• Apply guardrails, including PII masking with Sensitive Data Protection (DLP), safety filters, and least-privilege IAM.
• Work in Agile sprints with QA, UI, and data teams, and write clean, tested, documented code.
• Own features from prototype through production.

Benefits

• Paid time off, including vacation (12–25 days depending on employee grade), company-paid holidays, personal days, and sick leave.
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada).
• Retirement savings plans, such as a 401(k) in the U.S. or RRSP in Canada.
• Life and disability insurance.
• Employee assistance programs.
• Other benefits as provided by local policy and eligibility.
• The role may be eligible for variable incentives, bonuses, or commissions, depending on the position and applicable laws.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed