Python AI/LLM and data architecture audit and advisory
Hiring evidence involving Python, FastAPI, OpenAI API. First observed 20 Oct 2026; the response opens on upwork.com.
- Budget
- Not provided by source
- Work format
- Hiring
- Role family
- Software specialist
- Seniority
- Level not stated
- Observed
- 20 Oct 20262 days old
- Deadline status
- Deadline not provided — verify source
- Response destination
- upwork.com
Source job details
Structured facts are published only when supplied by the source; missing values are never invented.
- Hiring organization
- Undisclosed client
- Source reference
- OPP-20261020-1809
- Location evidence
- Not disclosed
- Employment type
- contractor
- Required skills
- Python; FastAPI; OpenAI API; AI agents; RAG; NLP; APIs
- Expected duration
- Less than30h/week;1–3months
Opportunity summary
Python AI/LLM and data architecture audit and advisory. The source classifies this as hiring work involving Python, FastAPI, OpenAI API, RAG, AI agents and agentic workflows. Location or remote eligibility: Remote listing; Ukraine and agency eligibility not confirmed.
Technology and scope
The reported stack is Python; FastAPI; OpenAI API; AI agents; RAG; NLP; APIs. Check the source for required versions, deliverables and which technologies are mandatory.
Engagement details
Hiring: an employment-oriented search for an individual contributor or team member. Source timing: Less than30h/week;1–3months
Budget
Budget not provided by source. Confirm the rate or project value before preparing a proposal; no estimate has been substituted.
Dates and availability
First observed by Leadiy on 20 Oct 2026, 16:57 UTC. Public since 23 Oct 2026, 00:00 UTC. Deadline not provided — verify source. A listed deadline does not confirm that applications are still accepted. These are observation and publication dates, not a reconstruction of the source posting date. Date-only deadlines have no supplied timezone; the date changes here at midnight UTC.
Related market context
In the current 30-day Hiring Pulse, Python appears in 181 of 593 observed opportunities (30.5%). Counts are multi-label, so one opportunity may contribute to several technology buckets. This is a separate aggregate sample, not a confirmation of this opportunity’s availability.
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