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# Data / Gen AI Engineer Hartford, CT

- **Company:** New York, Inc.
- **Location:** East Hartford, CT, United States
- **Contract:** Contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Cloud Storage, Continuous Integration, Data Stores, Data Flow Control, Identity and Access Management, Python (Programming Language), Scrum Methodology, Gemini Enterprise (Agent Platform), Search Technologies, Google Cloud, Google Data Studio, Retrieval-Augmented Generation, Flask (Web Framework), Generative AI, Apigee, Build Server, Vertex AI Vector Search, Git, Fastapi, Pytest, Pgvector, Google Gemini, Model Context Protocol, Terraform, Intelligent Document Processing, Looker Analytics, GPT, Docker, Agent2Agent Protocol
- **Published:** November 5, 2026
- **Apply:** https://www.dice.com/job-detail/4dcce62b-0903-4937-b318-ab477da56c69

## About the Role

4-8 years in software data ML engineering 1-5 years hands-on GenAI, Strong Python Fast API or Flask async pytest

Handson 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

Google Cloud Platform core Cloud Run Cloud Functions Cloud Storage BigQuery IAM Secret Manager

REST API design Git Docker CICD

Good written and verbal communication with onsite and client teams

Nice to have

Insurance domain PC ES 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 finetuning on Gemini and model monitoring

Terraform Looker or Looker Studio dashboards

## Description

We are looking for a hands-on Data Engineer / GenAI Engineer to build, test, and deploy production-ready Generative AI solutions on Google Cloud Platform (Google Cloud Platform) for insurance clients., 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 CICD in Cloud Build

Move and store data with BigQuery Cloud Storage PubSub 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