Python Engineer for Advanced AI Aim Assistant

AI Aim Assistant Project

Looking for exceptional Python Computer Vision engineer to fix my professional AI aim assistance system. This is a technically demanding project requiring expertise in real-time vision systems, ultra-low latency input control, and cross-platform development. To work on multiple different machines


⸻


Technical Requirements You Must Address

In your proposal, explain exactly how you'll implement:

1. **YOLO v5-v11 Integration**
- Dynamic model switching system
- Real-time GPU optimization with CPU fallback
- Memory-efficient inference pipeline

2. **1000Hz Input Control**
- Sub-millisecond controller input
- DirectInput/vJoy implementation
- Vigembuss
- Anti-recoil compensation algorithms

3. **Cross-Platform Support**
- High-performance screen capture
- Platform-specific input handling
- Remote play integration (Chiaki, Steam Link)

4. **Advanced PID Control**
- Smooth predictive aiming
- Anti-recoil pattern learning
- Rapid-fire modulation

5. **Professional UI**
- Real-time Pyaide6 overlay already made!
- Interactive 3D body targeting
- Live performance monitoring


⸻


Required Expertise

Essential Skills:
• Advanced Python with Computer Vision focus
• YOLO model deployment and optimization
• Real-time systems (<50ms latency)
• DirectInput and controller programming
• Cross-platform development
• PySide6 interface design


Preferred:
• Gaming industry experience
• Machine learning optimization
• Remote desktop protocols


⸻


Project Requirements

Phase 1: Core Architecture
• YOLO model abstraction layer
• Real-time screen capture system
• 1000Hz input controller framework
• Cross-platform compatibility layer
• Performance monitoring system


Phase 2: Advanced Features
• PID controller with anti-recoil
• Rapid-fire modulation system
• Remote play compatibility
• GPU optimization (TensorRT/ONNX)
• Security/anti-detection features


Phase 3: UI & Integration
• Modern PyQt6 application
• Real-time overlay system
• 3D body diagram selector
• Settings configuration panels
• Performance monitoring dashboard


⸻


Game Compatibility Requirements

Technical Integration:
• Window detection and targeting
• Game-specific recoil patterns
• Resolution and FOV scaling
• Custom config profiles per game


⸻


Selection Criteria

Your proposal MUST include:

1. **Technical Implementation Plan**
- Detailed architecture explanation
- Specific libraries and frameworks
- Performance optimization strategies
- Cross-platform compatibility approach

2. **Portfolio Evidence**
- Links to similar Computer Vision projects
- Performance benchmarks from previous work
- Code samples demonstrating relevant skills
- GitHub repository with professional projects

3. **Development Approach**
- Methodology for testing and validation
- How you'll achieve 1000Hz input response
- GPU optimization techniques for YOLO models
- Anti-detection implementation strategy



⸻


Auto-Rejection Criteria

We will NOT consider proposals that:
• Use generic templates without specific technical details
• Lack experience with real-time computer vision systems
• Cannot demonstrate YOLO model integration experience
• Don't address the 1000Hz input requirement specifically
• Fail to explain cross-platform compatibility approach
• Skip anti-recoil and rapid-fire implementation details
• Don't include portfolio evidence of similar projects


⸻


What We're Looking For

The ideal candidate will:
• Have built real-time Computer Vision applications before
• Understand gaming input systems and latency requirements
• Be able to optimize YOLO models for GPU inference
• Experience with DirectInput and controller programming
• Deliver professional-grade code with proper documentation
• Communicate technical concepts clearly and precisely