> Markdown version of [/jobs/ext/2165526-gen-ai-developer](https://www.wearedevelopers.com/jobs/ext/2165526-gen-ai-developer). 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).

---

# Gen AI Developer

- **Company:** Visionary Innovative Technology Solutions LLC
- **Location:** Tampa, FL, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Computing, Databases, Python (Programming Language), Search Technologies, Software Engineering, Workflow Management Systems, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Software Application Programming, Generative AI, Backend, Fastapi, Low Latency, Virtual Agents
- **Published:** August 21, 2026
- **Apply:** https://www.dice.com/job-detail/60b8ec90-56d5-4f1c-8ac1-f7b7de61cf27

## About the Role

We are looking for an experienced GenAI Developer with strong hands-on experience in building and deploying Generative AI and LLM-powered applications. The ideal candidate should have expertise in Python, RAG, prompt engineering, AI agents, LangChain/LangGraph, vector databases, and cloud-based AI services.

Comparable current roles emphasize production-grade GenAI development with Python, LLMs, RAG, vector search, agentic workflows, FastAPI, and cloud deployment., * 3+ years of experience in software development, with hands-on GenAI/AI experience.

 * Strong proficiency in Python.
 * Hands-on experience with Generative AI and LLM applications.
 * Strong knowledge of Prompt Engineering.
 * Experience designing and developing RAG pipelines.
 * Experience with LangChain and/or LlamaIndex.
 * Experience with LangGraph or Agentic AI frameworks is highly preferred.
 * Experience with:
 + Embeddings
 + Semantic Search
 + Vector Search
 + Document Processing
 + Knowledge Retrieval
 * Experience with vector databases such as:
 + Pinecone
 + FAISS

## Description

* Design and develop enterprise-grade Generative AI applications.
 * Build applications using Large Language Models (LLMs) such as OpenAI, Claude, Gemini, Llama, or other foundation models.
 * Develop and implement RAG (Retrieval-Augmented Generation) solutions.
 * Build document ingestion and processing pipelines, including chunking, embeddings, retrieval, and response generation.
 * Design and optimize prompts for different business use cases.
 * Develop AI agents and Agentic AI workflows.
 * Implement multi-agent systems, tool calling, function calling, and workflow orchestration.
 * Work with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.
 * Integrate LLMs with enterprise applications, databases, APIs, and external tools.
 * Develop scalable backend services using Python and FastAPI.
 * Work with vector databases for semantic search and knowledge retrieval.
 * Evaluate and improve LLM response quality, accuracy, latency, reliability, and cost.
 * Implement AI security, guardrails, validation, and responsible AI practices.
 * Deploy and support GenAI applications in production environments.
 * Collaborate with Data Engineers, ML Engineers, Software Developers, Architects, and Product teams.


## Related Videos

- [Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps](https://www.wearedevelopers.com/videos/966-accelerating-genai-development-harnessing-astra-db-vector-store-and-langflow-for-llm-powered-apps)
- [Kubernetes and Microservices with Multi-Model Databases](https://www.wearedevelopers.com/videos/382-kubernetes-and-microservices-with-multi-model-databases)
- [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock)
- [Intro to FastAPI](https://www.wearedevelopers.com/videos/462-intro-to-fastapi)
- [Fault Tolerance and Consistency at Scale: Harnessing the Power of Distributed SQL Databases](https://www.wearedevelopers.com/videos/1146-fault-tolerance-and-consistency-at-scale-harnessing-the-power-of-distributed-sql-databases)
- [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean)

## Related Articles

- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)
- [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering)