Roles & Responsibilities:
- Model Development: Design and implement deep learning models, focusing on Generative AI applications like text generation, image synthesis, personalized recommendations, or autonomous decision-making.
- Fine-tune and adapt pre-trained models (e.g., GPT, DALL-E, Stable Diffusion) for specific tasks.
- Develop foundational components of multi-agent systems where agents use AI to collaborate or solve problems.
- Multi-Agent Integration: Build and test individual AI agents and integrate them into a multi-agent framework using libraries such as Ray, OpenAI API, or custom architectures.
- Design communication protocols between agents and their environment.
- End-to-End Deployment: Contribute to the deployment of at least one Generative AI model or a multi-agent application in production, ensuring scalability and performance.
- Collaboration and Research: Work closely with cross-functional teams to integrate Generative AI models into multi-agent solutions.
- Stay updated with advancements in Generative AI and multi-agent systems and experiment with cutting-edge technologies.
- Documentation: Maintain detailed documentation of experiments, models, and processes for reproducibility and team collaboration.
Essential Skills:
- 5+ years of experience
- Proficiency with PyTorch, TensorFlow, or similar frameworks.
- Experience with LLM fine-tuning, prompt engineering, and model optimization.
- Familiarity with multi-agent frameworks like Ray, LangChain, or custom architectures.
- Working knowledge of distributed systems and cloud platforms (AWS, GCP, Azure).