AI Developer (Claude + Python) - Bradenton / Sarasota
Description
UF Resources · Bradenton, FL (On-Site, near UTC and I-75) · Full-Time W-2 · Pay based on experience, paid weekly
No commute to Tampa and no remote job in another time zone: build Claude-powered tools on-site in Bradenton.
Local candidates only: you must live in the Bradenton–Sarasota area now. No relocation, remote, or hybrid.
Build Claude-powered AI agents for internal tools, customer experiences, and creative workflows, with a small team and senior mentorship.
What you'll do
- Build Claude-powered workflows and agents in Python
- Integrate LLM APIs with REST APIs, databases, and web apps
- Iterate on prompts and context; build RAG-style workflows
- Ship clean code with tests, logging, and error handling
Required
- 1+ year of applied experience building with LLM APIs
- Solid Python, REST APIs, and JSON; Git
- Working knowledge of prompts, tokens, context windows, and hallucinations
Benefits: health, dental, vision, and life after 90 days; paid holidays, PTO, vacation; 401(k) with company match.
To apply: include a link to something you built with an LLM API and 2–3 sentences on a problem you solved. No project link, no review.
Skills
As published by workable · 7 questions · 2 written answers
Basics
First name, Last name, Email, Headline, Phone, Address, Education, Experience, Summary, Resume, Cover letter
Short answers (1)
- Please share a link to a project you built with an LLM API (GitHub, demo, or write-up).
Pick from a list (4)
- Years of experience in the role of an appointment setter optional
- Do you reside in the United States?
- Can you work on-site, full time, with no remote or hybrid option?
- Do you have at least 1 year of applied experience building with LLM APIs (Claude, OpenAI, Gemini, or similar)?
Written answers (2)
- This is an IN HOUSE POSITION. You MUST live in or near the Manatee County, FL area. Please indicate your city and state. PLEASE NOTE: THIS IS NOT A YES/NO QUESTION. PROVIDE CITY AND STATE.
- In 2–3 sentences, describe one problem you solved in an LLM project and how you solved it.