1. Research & Development of GenAI Tools

• Identify, evaluate, and benchmark state-of-the-art Generative AI tools (e.g., OpenAI, Stability AI, RunwayML, ElevenLabs, Pika Labs, and other open-source tools).
• Stay updated with emerging AI models in video synthesis, face manipulation, deepfake detection, and text-to-video technologies.
• Experiment with new model architectures, APIs, and frameworks for scalable content generation.

2. Orchestration & Automation

• Design automated workflows for orchestrating multiple GenAI tools to generate videos at scale.
• Develop pipelines integrating text, audio, and video generation models (e.g., combining LLMs with synthetic media tools).
• Optimize GPU/Cloud-based processing for efficient batch generation of synthetic datasets.
• Ensure seamless data pipeline integration for AI/ML model training.

3. Synthetic Data Generation for Deep Fake Classification

• Generate large-scale synthetic deep fake datasets using various AI-driven tools.
• Develop procedural rules to create diverse video content, mimicking real-world deep fake patterns.
• Implement labeling and annotation workflows to tag deepfake and real video content accurately.
• Work with ML engineers to improve dataset quality for deepfake classifiers.

4. Data Analysis & Performance Monitoring

• Analyze video synthesis outputs to assess realism, quality, and AI model bias.
• Conduct data-driven experiments to measure the effectiveness of generated datasets.
• Develop dashboards, reports, and insights to track synthetic data performance.
• Identify and troubleshoot model weaknesses and anomalies in deepfake detection.

5. Data Feed Development

• Design and implement data feeds from external sources, ensuring accuracy, reliability, and efficiency.
• Develop automated processes for data collection and ingestion, utilizing appropriate tools and technologies.

6. Data Scraping and Analysis

• Conduct data scraping from diverse external sources to gather relevant information.
• Perform heuristic analysis and data exploration to derive insights for better prioritization of tasks and projects.

7. Collaboration and Communication:

• Work effectively with a diverse team of multicultural freelancers, fostering a collaborative and inclusive work environment.
• Maintain clear and open communication channels to facilitate seamless coordination and feedback exchange.

REQUIREMENTS
Technical Skills

• Expertise in SQL: applicants should possess strong SQL skills, able to design, query, and manage databases effectively, with a focus on data manipulation and optimization
• Experience with data processing libraries: Candidates should have practical experience with PySpark and/or Pandas for data processing and analysis. Proficiency in handling large datasets and performing complex data transformations is essential
• Familiarity with AWS Services: Knowledge of AWS cloud services is required, including but not limited to EC2, S3, Lambda
• Gen AI experience

Soft Skills

• Independent: The ideal candidate should be able to work independently with minimal supervision, efficiently manage their workload, and make informed decisions
• Proactive: We are looking for individuals who are proactive in nature, always looking for ways to improve processes, solve problems before they escalate, and take initiative in their work
• Self-Learner: The ability to learn new technologies and methodologies quickly and effectively is essential. Candidates should demonstrate a strong capacity for self-directed learning and staying current with industry trends
• Advantages
• Candidates with experience in using Databricks for data engineering and analysis will have an advantage. Candidates who’ve served in the intelligence force as malware / cyber security analysts have an advantage.