1. Generative AI Solution Development:
- Develop and implement advanced solutions using Generative AI technologies, leveraging frameworks such as Python, LangChain, and HuggingFace.
- Engage in prompt engineering, working with Large Language Models (LLMs) to create tailored AI-driven solutions.
- Ensure response accuracy of prompts. Make sure the prompts generate desired outcomes and adhere to instructions.
2. Model Fine-Tuning and Optimization:
- Fine-tune pre-trained models from HuggingFace and other platforms to meet project-specific requirements.
- Optimize AI models for efficiency, accuracy, and scalability in educational contexts.
- Achieve minimum accuracy thresholds for models
- Reduce model training time and computational resource usage.
- Innovate AI techniques or algorithms at least once per quarter.
3. Collaboration with Business Teams:
- Collaborate with business stakeholders to align AI solutions with organizational goals, translating business requirements into technical specifications.
- Participate in cross-functional collaboration meetings, ensuring effective communication of AI model capabilities.
4. AI Frameworks and Tooling:
- Utilize frameworks like LangChain, Cohere, Keras, TensorFlow, and PyTorch for developing, testing, and deploying AI models.
- Integrate AI solutions into existing systems for seamless user experiences.
- Ensure AI deployment times are within defined timelines (e.g., from training to production).
- Maintain model latency below a defined threshold for real-time inference (e.g., under decided milliseconds).
5. Research and Innovation:
- Stay updated on the latest advancements in Generative AI, machine learning, and deep learning technologies.
- Experiment with current methodologies and emerging AI technologies to innovate and enhance existing solutions.
- Introduce innovative AI algorithms or approaches at least once per quarter.
6. System Architecture and Integration:
- Design robust system architec