AI/ML Developer for Video Tagging System
Budget: €750 – €1,500 EUR
Project: AI-Powered Video Tagging & Search Tool
(with Human-in-the-Loop Learning & Cloud Infrastructure Setup)
Overview
We are hiring an experienced AI/ML Developer to build a video tagging and segmentation system using a commercial AI platform (Google Cloud Video Intelligence, AWS Rekognition, Clarifai, Azure Video Indexer, etc.).
The system must analyze videos from our media library, automatically tag and extract relevant segments, and support human-in-the-loop feedback for continuous machine learning improvement.
The platform should be deployed on a scalable cloud infrastructure, and ideally connect with Dropbox, which we currently use for video storage (optional).
Use Cases
Brand A: Oscar Ono – luxury wooden flooring & interiors
- Tags: luxury, wooden floor, design, soft colors, interior
Brand B: La Vue – large group villa for families, sports, and business retreats
- Tags: family, nature, outdoor, hiking, group, team-building
Project Scope
1. Evaluate & recommend the best AI video tagging platform (Google, AWS, Azure, Clarifai, etc.)
2. Set up cloud infrastructure for the selected platform:
- Preferred: Google Cloud Platform, AWS, or Azure
- Configure storage (GCS, S3, or equivalent)
- Set up authentication (OAuth / service accounts)
- Optional: Dropbox integration for importing/storing video files
3. Develop a tagging tool that:
- Scans videos
- Detects scenes and assigns relevant tags
- Extracts timestamped segments or clip ranges
4. Build a simple search interface to filter and retrieve relevant clips by tag
5. Develop a human-in-the-loop validation interface:
- Users can confirm, correct, or reject AI-generated tags
- Feedback is stored and used to improve tagging quality over time
6. Deliver full documentation for internal use
Deliverables
- Platform comparison & recommendation
- Working prototype with tag-based video segment extraction
- Fully operational cloud infrastructure
- Human-in-the-loop workflow (UI or script-based)
- Optional: Dropbox sync or import feature
- Documentation (usage + tech setup)
Required Skills
- Strong experience with video analysis tools: Google Cloud Video API, AWS Rekognition, Clarifai, etc.
- Experience with Python or Node.js for backend/API work
- Cloud deployment skills (GCP, AWS, Azure)
- Comfortable working with APIs and storage integrations (Dropbox a bonus)
- Experience with ML workflows and human-in-the-loop design
- Familiarity with content tagging, scene detection, and media processing
Nice to Have
- Experience with CVAT, Label Studio, or other annotation tools
- Frontend experience (basic UI for validation/search)
- Experience with media asset management or digital archives
Timeline
Initial implementation: 4–6 weeks
Possible extension for training and support
Budget
Open to proposals — based on experience and scope
Please include:
- Short overview of your relevant experience
- Tools/platforms you propose and why
- Examples of similar work (video/ML/cloud)
- Timeline and price (hourly or fixed)
Test Task
To help evaluate technical fit, please include the following with your proposal:
1. Choose one of the following tools: Google Cloud Video Intelligence, Clarifai, or AWS Rekognition
2. Write a short 1-page outline describing:
- How you would set up the tool
- How you would integrate it with a video library stored on Google Cloud or Dropbox
- How you would extract segments that match keywords like "luxury", "outdoor", etc.
- How you would implement a basic feedback mechanism for human validation
Screenshots or links to GitHub repos or demos (if available) are a plus.
Apply Now
We’re ready to move forward quickly. Submit your application and let us know how you would approach this project.
to get idea of the two brands: A) Oscar Ono: oscarono.com B) La Vue: lavue.be
(with Human-in-the-Loop Learning & Cloud Infrastructure Setup)
Overview
We are hiring an experienced AI/ML Developer to build a video tagging and segmentation system using a commercial AI platform (Google Cloud Video Intelligence, AWS Rekognition, Clarifai, Azure Video Indexer, etc.).
The system must analyze videos from our media library, automatically tag and extract relevant segments, and support human-in-the-loop feedback for continuous machine learning improvement.
The platform should be deployed on a scalable cloud infrastructure, and ideally connect with Dropbox, which we currently use for video storage (optional).
Use Cases
Brand A: Oscar Ono – luxury wooden flooring & interiors
- Tags: luxury, wooden floor, design, soft colors, interior
Brand B: La Vue – large group villa for families, sports, and business retreats
- Tags: family, nature, outdoor, hiking, group, team-building
Project Scope
1. Evaluate & recommend the best AI video tagging platform (Google, AWS, Azure, Clarifai, etc.)
2. Set up cloud infrastructure for the selected platform:
- Preferred: Google Cloud Platform, AWS, or Azure
- Configure storage (GCS, S3, or equivalent)
- Set up authentication (OAuth / service accounts)
- Optional: Dropbox integration for importing/storing video files
3. Develop a tagging tool that:
- Scans videos
- Detects scenes and assigns relevant tags
- Extracts timestamped segments or clip ranges
4. Build a simple search interface to filter and retrieve relevant clips by tag
5. Develop a human-in-the-loop validation interface:
- Users can confirm, correct, or reject AI-generated tags
- Feedback is stored and used to improve tagging quality over time
6. Deliver full documentation for internal use
Deliverables
- Platform comparison & recommendation
- Working prototype with tag-based video segment extraction
- Fully operational cloud infrastructure
- Human-in-the-loop workflow (UI or script-based)
- Optional: Dropbox sync or import feature
- Documentation (usage + tech setup)
Required Skills
- Strong experience with video analysis tools: Google Cloud Video API, AWS Rekognition, Clarifai, etc.
- Experience with Python or Node.js for backend/API work
- Cloud deployment skills (GCP, AWS, Azure)
- Comfortable working with APIs and storage integrations (Dropbox a bonus)
- Experience with ML workflows and human-in-the-loop design
- Familiarity with content tagging, scene detection, and media processing
Nice to Have
- Experience with CVAT, Label Studio, or other annotation tools
- Frontend experience (basic UI for validation/search)
- Experience with media asset management or digital archives
Timeline
Initial implementation: 4–6 weeks
Possible extension for training and support
Budget
Open to proposals — based on experience and scope
Please include:
- Short overview of your relevant experience
- Tools/platforms you propose and why
- Examples of similar work (video/ML/cloud)
- Timeline and price (hourly or fixed)
Test Task
To help evaluate technical fit, please include the following with your proposal:
1. Choose one of the following tools: Google Cloud Video Intelligence, Clarifai, or AWS Rekognition
2. Write a short 1-page outline describing:
- How you would set up the tool
- How you would integrate it with a video library stored on Google Cloud or Dropbox
- How you would extract segments that match keywords like "luxury", "outdoor", etc.
- How you would implement a basic feedback mechanism for human validation
Screenshots or links to GitHub repos or demos (if available) are a plus.
Apply Now
We’re ready to move forward quickly. Submit your application and let us know how you would approach this project.
to get idea of the two brands: A) Oscar Ono: oscarono.com B) La Vue: lavue.be