> Markdown version of [/jobs/ext/3702549-genai-engineer](https://www.wearedevelopers.com/jobs/ext/3702549-genai-engineer). 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).

---

# GenAI Engineer

- **Company:** TechniPros, LLC
- **Location:** Columbus, OH, United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Adobe Flash, Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Cloud Computing, Cloud Storage, Computer Programming, Software Debugging, Distributed Systems, Identity and Access Management, Python (Programming Language), Machine Learning, NoSQL, Standard Sql, TypeScript, Pinecone, Qdrant, Google Cloud, Enterprise Software Applications, LangChain, ReactJS, Retrieval-Augmented Generation, Transfer Learning, Flask (Web Framework), Large Language Models, Multi-Agent Systems, Prompt Engineering, Hallucination Detection, Generative AI, Agentic-AI, Vertex AI Vector Search, Fastapi, Build Management, AI Platforms, Few-Shot Learning, HuggingFace, Google Cloud Functions, Weaviate, Milvus, Machine Learning Operations, Front End Software Development, Google Gemini, Restful APIs, Data Pipelines, Automation Anywhere, ChromaDB
- **Published:** October 12, 2026
- **Apply:** https://www.dice.com/job-detail/2d01d409-f6b3-48a9-a4f8-4b14d885da77

## About the Role

* Strong programming experience in Python.
 * Experience building REST APIs using FastAPI or Flask.
 * Hands-on experience with Google Gemini APIs, Vertex AI, and other enterprise LLM platforms.
 * Strong expertise with Langchain and LangGraph.
 * Experience implementing RAG architecture.
 * Strong knowledge of Google Cloud Platform (Google Cloud Platform).
 * Experience with Vertex AI, IAM, Cloud Run, BigQuery, and Google Cloud Storage.
 * Experience working with Vector Databases including Pinecone, Weaviate, Qdrant, Chroma, or Milvus.
 * Strong SQL and NoSQL database experience.
 * Experience debugging complex AI pipelines and distributed applications.
 * Strong problem-solving and communication skills.

Preferred Qualifications:

 * Google Cloud Professional Machine Learning Engineer Certification.
 * Google Cloud Professional Cloud Architect Certification.
 * Experience with Llama Index.
 * Experience with Hugging Face.
 * Experience with React and TypeScript.
 * Knowledge of Agentic AI architectures.
 * Experience with MLOps or LLMOps platforms., * Python
 * Google Gemini
 * Vertex AI
 * Google Cloud Platform (Google Cloud Platform)
 * Langchain
 * LangGraph
 * FastAPI
 * Flask
 * RAG
 * Prompt Engineering
 * Vector Databases
 * Pinecone
 * Weaviate
 * Qdrant
 * Chroma
 * Milvus
 * BigQuery
 * Cloud Run
 * Google Cloud Storage
 * REST APIs

Preferred Skills:

 * Llama Index
 * Hugging Face
 * React
 * TypeScript
 * PEFT

## Description

We are looking for an experienced GenAI Engineer to design and build next-generation AI applications using Google Gemini, Vertex AI, and the Google Cloud Platform (Google Cloud Platform) ecosystem. The ideal candidate will have strong expertise in LangChain, LangGraph, Retrieval-Augmented Generation (RAG), agentic AI workflows, and scalable cloud-native architectures. This role involves building production-grade AI solutions, integrating LLMs into enterprise applications, and developing intelligent multi-agent systems., * Design, develop, and deploy Generative AI applications powered by Google Gemini (Pro, Flash, Ultra) and Vertex AI.

 * Build advanced prompt pipelines, RAG applications, and AI workflows using LangChain.
 * Design and implement stateful, multi-agent AI systems using LangGraph.
 * Develop scalable AI solutions utilizing Google Cloud services including Vertex AI Search, BigQuery, Cloud Run, Cloud Storage, and IAM.
 * Build robust data ingestion pipelines supporting multiple document formats.
 * Implement vector search architectures using Vertex AI Vector Search or vector databases such as Chroma, Milvus, Pinecone, Weaviate, or Qdrant.
 * Optimize LLM performance using prompt engineering, few-shot learning, and PEFT techniques.
 * Establish evaluation metrics for LLM accuracy, latency, hallucination detection, and model performance.
 * Implement LLMOps best practices including observability, scalability, monitoring, and security.
 * Develop REST APIs using FastAPI or Flask to expose AI services.
 * Collaborate with Product Managers, Data Engineers, and Front-End Developers to integrate AI capabilities into enterprise applications.