Autonomous Offline Python AI Development
Budget: $5,000 – $10,000 AUD
Title:
Build Offline Python-Based AI System with File Watcher, Reflection, and Simulation Logic
Description:
Looking for an experienced Python/AI developer to help build a modular, offline AI system that can operate fully autonomously and learn over time.
This is not a typical chatbot or cloud deployment — this system will run on a GPU-powered local environment (RTX 5080) and integrate the following components:
Core Modules Needed:
File Watcher + Ingestion
Monitor folders for new PDFs
Parse content (OCR fallback ideal)
Auto-summarize and generate threat-based Q&A
Memory System
Store extracted insights in structured files
Append to long-term memory logs
Reflection Engine
Auto-trigger summaries or reflections based on time/events
Generate insights, highlight patterns, and escalate priority topics
Simulation Framework
Run cyber scenarios with escalating difficulty
Auto-score responses and adjust future difficulty
Local Chat Interface
Use a quantized local model (DeepSeek, LLaMA, or similar)
Support config prompts, memory integration, and reflection triggers
UI (Minimal but functional)
File upload
Chat window
System status (memory, logs, GPU usage)
Must-Have Experience:
Python (multi-module, async/process mgmt)
Local LLM deployment (DeepSeek, LLaMA.cpp, or similar)
FastAPI or backend integration
GPU compute (CUDA / inference tuning)
Simulation design or gamified AI training (bonus)
Environment:
All work will run fully offline
Target device has RTX 5080, 64GB RAM
Needs to run headlessly and be Docker-deployable (optional)
Delivery Model:
Push code regularly to private GitHub repo
Milestone-based payments
You must show proof of real system logic (no screenshots-only developers)
Budget: Open depending on experience. Quality and system robustness will be prioritized over lowest cost. Looking for developers who can think ahead and build intelligently, not just implement to spec.
To Apply, Include:
Examples of similar systems or logic you've built (repos/screenshots)
Experience with offline LLMs, memory handling, or simulation engines
Availability and estimated time to deliver a working MVP
Build Offline Python-Based AI System with File Watcher, Reflection, and Simulation Logic
Description:
Looking for an experienced Python/AI developer to help build a modular, offline AI system that can operate fully autonomously and learn over time.
This is not a typical chatbot or cloud deployment — this system will run on a GPU-powered local environment (RTX 5080) and integrate the following components:
Core Modules Needed:
File Watcher + Ingestion
Monitor folders for new PDFs
Parse content (OCR fallback ideal)
Auto-summarize and generate threat-based Q&A
Memory System
Store extracted insights in structured files
Append to long-term memory logs
Reflection Engine
Auto-trigger summaries or reflections based on time/events
Generate insights, highlight patterns, and escalate priority topics
Simulation Framework
Run cyber scenarios with escalating difficulty
Auto-score responses and adjust future difficulty
Local Chat Interface
Use a quantized local model (DeepSeek, LLaMA, or similar)
Support config prompts, memory integration, and reflection triggers
UI (Minimal but functional)
File upload
Chat window
System status (memory, logs, GPU usage)
Must-Have Experience:
Python (multi-module, async/process mgmt)
Local LLM deployment (DeepSeek, LLaMA.cpp, or similar)
FastAPI or backend integration
GPU compute (CUDA / inference tuning)
Simulation design or gamified AI training (bonus)
Environment:
All work will run fully offline
Target device has RTX 5080, 64GB RAM
Needs to run headlessly and be Docker-deployable (optional)
Delivery Model:
Push code regularly to private GitHub repo
Milestone-based payments
You must show proof of real system logic (no screenshots-only developers)
Budget: Open depending on experience. Quality and system robustness will be prioritized over lowest cost. Looking for developers who can think ahead and build intelligently, not just implement to spec.
To Apply, Include:
Examples of similar systems or logic you've built (repos/screenshots)
Experience with offline LLMs, memory handling, or simulation engines
Availability and estimated time to deliver a working MVP
Related categories:
Python
Software Architecture
Machine Learning (ML)
Docker
Large Language Models (LLMs)