> Markdown version of [/jobs/ext/4577005-data-engineer-genai-google-cloud-platform](https://www.wearedevelopers.com/jobs/ext/4577005-data-engineer-genai-google-cloud-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - GenAI / Google Cloud Platform - **Company:** Lorven Technologies Inc - **Location:** Hartford, CT, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** LangGraph Framework, Testing (Software), Application Programming Interfaces (APIs), Artificial Intelligence, Optical Character Recognition (OCR), BigQuery, Cloud Storage, Computer Programming, Continuous Integration, Data Flow Control, Google Docs, Identity and Access Management, Information Extraction, Python (Programming Language), Machine Learning, Search Technologies, Software Deployment, Systems Integration, Google Cloud, Enterprise Software Applications, Retrieval-Augmented Generation, Flask (Web Framework), Prompt Engineering, Generative AI, Apigee, Build Server, Agentic-AI, Vertex AI Vector Search, Git, Fastapi, Pytest, Pgvector, Information Technology, Google Cloud Functions, Asynchronous Programming, Invoking Functions, Google Gemini, Restful APIs, Docker - **Published:** November 5, 2026 - **Apply:** https://www.dice.com/job-detail/88e2e9e6-6dc1-423c-a9c4-041f4f893d9c ## About the Role * Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related field, or equivalent experience. * 4-8 years of experience in Software, Data, or ML Engineering, with hands-on Generative AI experience. * Strong programming experience in Python, including FastAPI/Flask, asynchronous programming, and Pytest. * Hands-on experience building GenAI applications using Google Gemini, Gemini API, and Vertex AI. * Strong expertise in Prompt Engineering, Structured Outputs, Function Calling, and Multimodal AI. * Experience designing and implementing RAG pipelines, including document chunking, embeddings, retrieval, and grounding. * Hands-on experience with Vector Search, Vertex AI Search, AlloyDB, pgvector, or BigQuery Vector Search. * Experience developing AI agents using Google ADK, LangChain, or LangGraph and integrating external tools and APIs. * Strong knowledge of Google Cloud Platform (Google Cloud Platform) services, including Cloud Run, Cloud Functions, Cloud Storage, BigQuery, Pub/Sub, and Dataflow. * Experience with Google Document AI, OCR, document extraction, and classification. * Hands-on experience deploying applications using Docker, GKE, Cloud Build, CI/CD, and Apigee. * Experience with REST API development, Git, and enterprise application integrations. * Knowledge of GenAI evaluation, quality testing, latency optimization, and cost monitoring. * Understanding of AI security, guardrails, PII masking, DLP, and IAM access controls. * Strong communication skills and experience collaborating with onsite and cross-functional teams.